Eye Movements Biometrics: A Bibliometric Analysis from 2004 to 2019

06/01/2020
by   Antonio Ricardo Alexandre Brasil, et al.
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Person identification based on eye movements is getting more and more attention, as it is anti-spoofing resistant and can be useful for continuous authentication. Therefore, it is noteworthy for researchers to know who and what is relevant in the field, including authors, journals, conferences, and institutions. This paper presents a comprehensive quantitative overview of the field of eye movement biometrics using a bibliometric approach. All data and analyses are based on documents written in English published between 2004 and 2019. Scopus was used to perform information retrieval. This research focused on temporal evolution, leading authors, most cited papers, leading journals, competitions and collaboration networks.

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1 Introduction

The idea of using eye movements to identify people was first reported by the pioneering work of Kasprowski and Ober [45]. In their experiments, jumping dots displayed on the screen in specific intervals were used as a stimulus for the users. For a database of nine persons, the best result was an average false acceptance rate of 1.48% and an average false rejection rate of 22.59%. The popularity of eye movement as biometrics has grown for many reasons: it is resistant to anti-spoofing [61, 80] and can be used for continuous authentication of the user [68, 13, 49], as well as capturing non-invasive movements.

Currently, many institutions and researchers have been studying the potential of eye movements biometrics for the future and a series of competitions have been held to stimulate research in the field. To identify the evolution in the field, a quantitative approach, the bibliometric analysis, was chosen for the present study.

The bibliometric methodology was developed based on the statistical bibliography proposed by Hulme et al. [33], later developed by Pritchard et al. [70], and relies on the quantitative study of bibliographic records. It is part of librarianship and information science being used in several aspects like the selection of books and periodicals, the evaluation of bibliographies, and historical applications [64].

This article will provide useful information for researchers in the field of eye movement biometrics. To achieve this goal, this article will focus on the analysis of all records since the first paper about eye movement biometrics. The analysis will be on the 189 articles written in English and published from 2004 to 2019, indexed by Scopus.

This paper is organized as follows: Section 2 describes the methodological approach of this research. Section 3 presents the delimitations of the study. The results obtained and discussions of the present study in Section 4. Competitions in eye movement biometrics in Section 5. Finally, Section 6 presents the conclusion of our bibliometric study.

2 Methodological approach

To identify the development in the field of eye movement biometrics, this paper used the Scopus Database (created by Elsevier) [21]. A retrospective search on Scopus in the period 2004-2019. The search was performed using the query: TITLE-ABS-KEY (“biometric*” OR “person identification” OR “person recognition”) AND TITLE-ABS-KEY (“eye movement” OR “sacca*” OR “ocular movement” OR “gaze track*”) AND PUBYEAR 2003 AND PUBYEAR 2020 .

By using these terms, 242 documents were found, being 240 in English, 1 in Chinese, and 1 in Turkish. Only documents written in English were used, considering the overall scope of the language. The distribution of the 240 documents per type is: Conference Paper (125; 52.08%), Article (86; 35,83%), Conference Review (19; 7.92%), Book Chapter (6; 2.50%), Review (4; 1.67%). Documents with the type of Conference Review and Book Chapter were removed from the bibliometric analysis, just as Alvarez-Betancourt and Garcia-Silvestre [8] did, reaching 215 documents for analysis.

Figure 1: Articles using eye movement biometrics for each year, from 2004 to 2019. The x-axis shows the years from 2004 to 2019 and y-axis shows the number of publications.

Since the Scopus database is updated daily, all search results have been exported to the CSV file to save the results for future reference. Thereafter, a process of normalizing the names of the authors was performed, due to cases of duplicate author names (e.g. the author Oleg Komogortsev is sometimes just O. Komogortsev). In case the previous procedure failed to resolve ambiguities, additional information was retrieved from the profile of Google Scholar to clear any doubts.

It is not a qualitative study describing methods and techniques, neither a state-of-art of eye movement biometrics research. In this sense, an adequate methodological approach is necessary to identify in the current literature the promising of the research. For a study of the state-of-art, this work highly recommends reading the article [81]. The result of the search in the Scopus database brought articles that are not about eye movement like biometrics, although they are related themes. Some articles have even been listed as most cited in Table 3.2, but are marked with ’*’.

3 Results and discussion

The temporal evolution of the number of works in the field is presented in Figure 1. It is possible to see a solid and incremental amount of publications. The x-axis shows the years from 2004 to 2019 and the y-axis shows the numbers of publications. In the period of 2006 to 2008, there was no increase in the number of papers, but in the period of 2010 to 2013, it is possible to observe a solid growth in the number of articles. The blue line shows the growth trend in the years from 2004 to 2019.

3.1 Leading authors

From the 215 papers selected, 553 authors were identified, of which 22.60% published more than one paper. Statistical data from the 20 authors with the highest amount of production are shown in Table 3.1

. The first column is the ranking classified by the number of publications of the author; the second column is the name of the author; in the third column, the number of publications (P); the fourth column shows the number of collaborations (C) and; in the fifth column, the number of times the author was cited (R).

The 20 most productive authors, where P is the number of publications, C the number of collaborations and R the number of times that the author has been cited. Rank is given by R divided by P. The Betwe., Clos. and Deg. refers respectively to Betweenness, Closeness and, Degree. The last column presents all publications references’ for each author. The top three values in each column are in bold.

Author P C R R/P Rank h-index Betwe. Clos. Deg. References
1. Komogortsev, O. V. 34 21 94 2.76 18 14 0.03 0.14 0.29 [23, 65, 97, 75, 24, 81, 72, 1, 53, 76, 2, 63, 80, 61, 78, 77, 55, 56, 79, 52, 32, 31, 51, 58, 30, 12, 54, 44, 29, 62, 59, 60, 28, 57]
2. Holland, C. D. 14 9 75 5.35 7 8 0.00 0.08 0.03 [53, 61, 55, 56, 52, 32, 31, 51, 30, 54, 29, 62, 60, 28]
3. Rigas, I. 14 4 58 4.14 10 8 0.01 0.09 0.16 [75, 81, 72, 1, 76, 63, 80, 78, 77, 79, 38, 48, 74, 73]
4. Kasprowski, P. 13 2 58 4.46 9 8 0.00 0.01 0.10 [43, 41, 42, 26, 40, 38, 39, 48, 44, 36, 47, 45, 46]
5. Karpov, A. 10 10 60 6 4 8 0 0.10 0.03 [53, 61, 56, 52, 51, 58, 44, 62, 59, 60]
6. Juhola, M. 6 5 27 4.5 8 4 0 0.01 0.05 [100, 102, 101, 99, 98, 35, 103]
7. Zhang, Y. 6 1 57 9.5 3 3 0.00 0.01 0.07 [100, 102, 101, 99, 98, 35, 103]
8. Martinovic, I. 6 6 34 5.6 5 4 0 0.03 0.03 [17, 16, 86, 19, 85, 18]
9. Deravi, F. 5 4 27 5.4 6 3 0.00 0.01 0.03 [4, 7, 6, 5, 15]
10. Harezlak, K. 5 4 15 3 15 3 0.00 0.01 0.05 [43, 41, 42, 26, 40]
11. Rasmussen, K.B. 5 5 19 3.8 12 3 0 0.03 0.03 [17, 86, 19, 85, 18]
12. Ali, A. 4 4 13 3.25 13 2 0 0 0.05 [4, 7, 6, 5]
13. Eberz, S. 4 0 16 4 11 3 0 0 0.10 [17, 16, 19, 18]
14. Hoque, S. 4 4 12 3 15 2 0 0.01 0.01 [4, 7, 6, 5]
15. Lenders, V. 4 4 13 3.25 13 3 0 0.01 0.01 [17, 16, 19, 18]
16. Friedman, L. 3 3 9 3 15 2 0.00 0.06 0.05 [23, 75, 24]
17. Hansen, D. W. 3 2 41 13.7 2 3 0.00 0.03 0.05 [9, 92, 25]
18. Ober, J. 3 3 89 29.7 1 3 0 0.02 0.01 [47, 45, 46]
19. Saeed, U. 3 0 4 1.33 19 2 0 0 0 [84, 83, 82]
20. Wang, X. 3 2 2 0.66 20 2 0.00 0.03 0.07 [66, 93, 94, 95]

In terms of the number of publications (P), the author with most publications is the researcher Oleg Komogortsev, professor at Texas State University, with 34 papers about the research field, twice as much as the numbers of papers of the second author. There are four more authors with more than ten papers: Ioannis Rigas (researcher at Gemalto Cogent Inc.); Pawel Kasprowski (professor at Politechnika Slaska); Corey D. Holland and Alex Karpov (Texas State University). The publication of these five authors sums 85 works, representing 39.53% of 215 works. Evaluating the number of collaborations (column C), researcher Oleg Komogortsev maintains first place with 21 collaborations, followed by researcher Karpov with 10 collaborations and Corey D. Holland with 9 collaborations. A detailed network analysis will be presented in the next section.

The most cited authors are (column R): Oleg Komogortsev (with 94 citations in his 34 works), Pawel Kasprowski (with 58 citations) and Corey D. Holland (with 58 citations). The sixth column of Table 3.1 shows the division (R/P) and; the seventh is the Rank based on the R/P rate. In this last column, position 2 repeats in the last two rows, which means that both authors are in the second position, according to R/P. When the repetition occurs, the next position is increased by one, in this case, skipping position number 3. In this simplified ranking, taking into the total number of publications divided by the total number of citations, the three authors are Jósef Ober (professor at the Silesian University of Technology); Dan Witzner Hansen (professor at the University of Copenhagen) and Youming Zhang.

Nevertheless, the index (R/P) is not so effective. For example, the authors Jósef Ober and Dan Witzner Hansen have three papers with a high number of citations. For Jósef Ober, these three papers were written in collaboration with Pawel Kasprowski, who had more publications and Dan Witzner Hansen published a survey of models for eyes and gaze, with high number of citations. Thus, for the relevance of publications, the h-index will be used. It is defined as the highest number of publications of a scientist that received ’h’ or more citations each while the other publications have not more than ’h’ citations each [27]. In this work, only the articles selected by the Scopus search will be considered. Thus, the h-index calculated by Google Scholar or another website will not be used, because in the profile of these websites the author’s h-index is calculated considering all the works published, regardless if it is about eye movements biometrics or not.

The calculated h-index for each author is shown in the eighth column of Table 3.1. The author with the highest h-index is Oleg Komogortsev with an h-index of 14, followed by Holland, Rigas, Kasprowski, and Karpov with 8.

3.2 Collaboration network

Figure 2: Collaboration network of works related to eye movement biometrics. Each node represents an author and each vertex represents the joint publication between the authors (nodes).

Top 15 most cited papers retrieved from Scopus. Rank Title Year Cited by 1 In the Eye of the Beholder: A Survey of Models for Eyes and Gaze* [25] 2010 835 2 Eye movements in biometrics [45] 2004 86 3 Clinical criteria for subtyping Parkinson’s disease: Biomarkers and longitudinal progression* [22] 2005 81 4 Eye-movements as a biometric [10] 2005 79 5 Ocular biometrics: A survey of modalities and fusion approaches [67] 2015 73 6 Biometric identification via eye movement scanpaths in reading [28] 2011 72 7 The use of rodent skilled reaching as a translational model for investigating brain damage and disease* [50] 2012 63 8 Biometric identification based on the eye movements and graph matching techniques [73] 2012 59 9 Multitask learning for EEG-based biometrics* [91] 2008 54 10 Towards task-independent person authentication using eye movement signals [49] 2010 53 11 Unaware Person Recognition From the Body When Face Identification Fails* [71] 2013 52 12 Biometric authentication via oculomotor plant characteristics [59] 2012 50 13 Biometric identification via an oculomotor plant mathematical model [52] 2010 47 14 Complex eye movement pattern biometrics: Analyzing fixations and saccades [31] 2013 43 15 Cross-database face antispoofing with robust feature representation* [69] 2016 42

Top 3 journals with most published works. Name IF # 1. Proceedings of SPIE - The International Society for Optical Engineering [88] 0.993 11

2. Pattern Recognition Letters

[20] 1.952 6 3. IEEE Transactions on Information Forensics and Security [87] 5.824 6

Top 5 conventions with the most publications. Name #

1. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

[90] 15 2. Eye Tracking Research and Applications Symposium (ETRA) [3] 5 3. International Journal of Biometrics [34] 4 4. 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012 [11] 4 5. Advances in Intelligent Systems and Computing [89] 3

To evaluate the collaborations between the authors, this work used an analysis based on a network of collaborations. Each node represents an author and each vertex represents the joint publication between the authors (nodes). The network of collaborations is presented in Figure 2. To generate the network, the authors of Table 3.1 and all their collaborators were selected – regardless of whether or not the author is in the list of most productive authors.

For the collaboration analysis, this work obtained the centrality measures to identify the pattern of the most productive authors [96, 8]. These metrics are: betweenness (column Betwe. of Table 3.1), closeness (column Clos. of Table 3.1) and degree (column Deg. of Table 3.1).

Degree centrality aims to measure the number of direct connections of a node within the network, being important to identify the collaboration network. The degree is the easiest measure to see in Figure 2 because a degree is the direct connection of the node and indicates the number of collaborations (C) shown in Table 3.1. The nodes with the highest degree are in red in Figure 2, Kasprowski, Komogortsev and Rigas, which cooperate with each other in the network. The degree is normalized by dividing by the maximum possible degree in the graph where N is the number of nodes in G.

Betweenness is a measure that, given all the nodes (authors), calculates the number of minimum paths using a geodesic distance from each node to all other nodes that connect through it. This metric aims to analyze the influence of an author to participate in the network, being a bridge between the parts of the network. Closeness measures the distance from one vertex to all other vertices. In this case, it is the distance from an author along with all other authors using a geodesic distance. It is an important metric to identify if an author is efficient, ie, if the author collaborates with other authors in the network. Closeness and betweenness values were normalized, considering the number of nodes in the connected part of the graph containing the node.

A different row of Table 3.1 refers to the author Saeed, whose metrics have betweenness, closeness, and degree with zero values. One of the reasons that this author has zero values in his metrics is because this author does not have any collaboration in the network, a fact that is possible to see in Figure 2.

Another analysis that can be made is the author Holland has a high h-index, but zero in betweenness. This is due to the author has many collaborations with Oleg Komogortsev, but no collaborations with other authors of the network. Ober, for example, has a closeness value of 0.0242, but zero betweenness, this is due Ober have been working only with Kasprowski and he has no interactions with other authors. The author with the highest metrics of collaborations is Oleg Komogortsev.

In the analysis of the collaboration network between institutions, there is the strong presence of Texas State University, with a large number of works, which is the institution of author Oleg Komogortsev.

3.3 Most cited papers

We made an analysis of the most cited articles retrieved from Scopus. Some papers retrieved in the Scopus search were not about eye movement biometrics, but are present in Table 3.2 with ’*’.

The first study of eye movement as biometric identification [45] is present in the table as Rank 2. It is possible to correlate the most cited papers with the most productive authors, for example, the fourth most productive authors Kasprowski, Komogortsev, Rigas and Holland, in Table 3.1 present the works most cited papers in the ranking, presented respectively in the ranking 2, 6, 8 and 12.

3.4 Leading journals and conventions

Considering all the collected works, a study was conducted to evaluate the journals and conventions that concentrated most publications. Table 3.2 lists the top 3 journals and Table 3.2 top 5 conventions, ranked in descending order. The impact factor (IF) has been retrieved from the journal’s page and its value reflects the average number of citations of articles recently published in the journal.

The journals and conventions vary from topics on artificial intelligence, bioinformatics, pattern recognition, biometrics, computer forensics, and security. All of this information is relevant and helps future researchers search for papers in the area or prospect in which journals and events have an affinity with the theme.

4 Competitions in eye movement biometrics

Although the competitions were not explicitly as a result of the search in the Scopus database, competitions boosted research in the area, with the aim to bring new researchers to the field and to collaborate with the results obtained over the years. Competitions have been identified through the titles of the articles collected.

The first competition was called EMVIC (Eye Movement Verification and Identification Competition)111http://www.emvic.org/ and was held in 2012 at the IEEE 5th International Conference on Biometrics: Theory Applications and Systems (BTAS) [44] in partnership with Kaggle website222http://www.kaggle.com/c/emvic. In this competition, 49 registered teams and 524 submissions were registered. The work of Cuong et al. [14]

extracted the eye features from eye raw data: cepstral coefficient, eye difference and eye velocity and, applied Decision tree, Bayesian network and Support vector machine and the results were an Identification Rate (IR) of 93.56% in Dataset A and 90.43% in Dataset B.

A second competition was launched, called EMVIC 2014 [37], and different from the first competition, used face images as a stimulus. The competition released 837 samples from 34 volunteers to the training dataset and 593 samples from 22 volunteers for the test dataset. There were 82 participants enrolled and 19 submissions, the best accuracy obtained was 39.63%. The result was very different from the first competition with lower submissions and a smaller dataset, which could be affected by the results.

Another competition with the same objective was launched one year later, called BioEye 2015333https://bioeye.cs.txstate.edu/ [63]. The competition had 64 competitors, a total of 200 submissions along 26 days. In this edition were released four datasets to evaluate different parameters of the algorithms.

The results obtained in BioEye 2015 demonstrated the potential of identification through eye movements, obtained in different scenarios [81]. Submissions were evaluated using the Rank-1 Identification Rate (IR), defined by the ratio of correct classifications with the amount of data.

5 Conclusion

This paper presented a bibliometric analysis of eye movement biometrics based on the selected papers in journals and conferences between the years 2004 to 2019 indexed by Scopus. An interesting point was the competitions that helped to grow more and more research in the area. The number of publications increased significantly in the competition years (2012, 2014 and 2015) helping the authors to make partnerships and continuous studies. Using statistical methods of bibliometry the work has able to identify the network of collaboration between institutions and authors, most relevant articles, journals and conventions.

References

  • [1] Evgeniy Abdulin, Ioannis Rigas, and Oleg Komogortsev. Eye movement biometrics on wearable devices: What are the limits? In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pages 1503–1509. ACM, 2016.
  • [2] Evgeniy R Abdulin and Oleg V Komogortsev. Person verification via eye movement-driven text reading model. In 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pages 1–8. IEEE, 2015.
  • [3] ACM. Acm symposium on eye tracking research & applications. https://etra.acm.org, 2019. Access: 01/01/2019.
  • [4] Asad Ali, Nawal Alsufyani, Sanaul Hoque, and Farzin Deravi. Gaze-based presentation attack detection for users wearing tinted glasses. In 2019 Eighth International Conference on Emerging Security Technologies (EST), pages 1–5. IEEE, 2019.
  • [5] Asad Ali, Farzin Deravi, and Sanaul Hoque. Spoofing attempt detection using gaze colocation. Lecture Notes in Informatics (LNI), 212.
  • [6] Asad Ali, Farzin Deravi, and Sanaul Hoque. Spoofing attempt detection using gaze colocation. In 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG), pages 1–12. IEEE, 2013.
  • [7] Asad Ali, Sanaul Hoque, and Farzin Deravi. Gaze stability for liveness detection. Pattern Analysis and Applications, 21(2):437–449, 2018.
  • [8] Yuniol Alvarez-Betancourt and Miguel Garcia-Silvente. An overview of iris recognition: A bibliometric analysis of the period 2000–2012. Scientometrics, 101(3):2003–2033, 2014.
  • [9]

    Fabricio Batista Narcizo and Dan Witzner Hansen. Depth compensation model for gaze estimation in sport analysis. In

    Proceedings of the IEEE International Conference on Computer Vision Workshops

    , pages 71–78, 2015.
  • [10] Roman Bednarik, Tomi Kinnunen, Andrei Mihaila, and Pasi Fränti. Eye-movements as a biometric. Image analysis, pages 16–26, 2005.
  • [11] IEEE Biometrics. Ieee international conference on biometrics: Theory, applications and systems. http://ieee-biometrics.org/index.php/conferences/btas, 2019. Access: 01/01/2019.
  • [12] Michael Brooks, Cecilia R Aragon, and Oleg V Komogortsev. Perceptions of interfaces for eye movement biometrics. In 2013 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2013.
  • [13] David Crouse, Hu Han, Deepak Chandra, Brandon Barbello, and Anil K Jain. Continuous authentication of mobile user: Fusion of face image and inertial measurement unit data. In Biometrics (ICB), 2015 International Conference on, pages 135–142. IEEE, 2015.
  • [14] Nguyen Viet Cuong, Vu Dinh, and Lam Si Tung Ho. Mel-frequency cepstral coefficients for eye movement identification. In Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on, volume 1, pages 253–260. IEEE, 2012.
  • [15] Farzin Deravi and Shivanand P Guness. Gaze trajectory as a biometric modality. In Biosignals, pages 335–341, 2011.
  • [16] Simon Eberz, Giulio Lovisotto, Andrea Patane, Marta Kwiatkowska, Vincent Lenders, and Ivan Martinovic. When your fitness tracker betrays you: Quantifying the predictability of biometric features across contexts. In 2018 IEEE Symposium on Security and Privacy (SP), pages 889–905. IEEE, 2018.
  • [17] Simon Eberz, Giulio Lovisotto, Kasper B Rasmussen, Vincent Lenders, and Ivan Martinovic. 28 blinks later: Tackling practical challenges of eye movement biometrics. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 1187–1199, 2019.
  • [18] Simon Eberz, Kasper B Rasmussen, Vincent Lenders, and Ivan Martinovic. Looks like eve: Exposing insider threats using eye movement biometrics. ACM Transactions on Privacy and Security (TOPS), 19(1):1–31, 2016.
  • [19] Simon Eberz, Kasper B Rasmussen, Vincent Lenders, and Ivan Martinovic. Evaluating behavioral biometrics for continuous authentication: Challenges and metrics. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pages 386–399, 2017.
  • [20] Elsevier. Pattern recognition letters. https://www.journals.elsevier.com/pattern-recognition-letters, 2019. Access: 01/01/2019.
  • [21] Elsevier. Scopus. https://www.elsevier.com/solutions/scopus, 2019. Access: 01/09/2019.
  • [22] Seyed-Mohammad Fereshtehnejad, Yashar Zeighami, Alain Dagher, and Ronald B Postuma. Clinical criteria for subtyping parkinson’s disease: biomarkers and longitudinal progression. Brain, 140(7):1959–1976, 2017.
  • [23] Lee Friedman and Oleg V Komogortsev. Assessment of the effectiveness of seven biometric feature normalization techniques. IEEE Transactions on Information Forensics and Security, 14(10):2528–2536, 2019.
  • [24] Lee Friedman, Mark S Nixon, and Oleg V Komogortsev. Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases. PloS one, 12(6):e0178501, 2017.
  • [25] Dan Witzner Hansen and Qiang Ji. In the eye of the beholder: A survey of models for eyes and gaze. IEEE transactions on pattern analysis and machine intelligence, 32(3):478–500, 2010.
  • [26] Katarzyna Harezlak, Tomasz Wasinski, and Pawel Kasprowski. The eye movement data storage–checking the possibilities. In International Conference on Intelligent Decision Technologies, pages 337–345. Springer, 2017.
  • [27] Jorge E Hirsch. An index to quantify an individual’s scientific research output. Proceedings of the National academy of Sciences of the United States of America, 102(46):16569, 2005.
  • [28] Corey Holland and Oleg V Komogortsev. Biometric identification via eye movement scanpaths in reading. In 2011 International joint conference on biometrics (IJCB), pages 1–8. IEEE, 2011.
  • [29] Corey D Holland and Oleg V Komogortsev. Biometric verification via complex eye movements: The effects of environment and stimulus. In 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 39–46. IEEE, 2012.
  • [30] Corey D Holland and Oleg V Komogortsev. Complex eye movement pattern biometrics: Analyzing fixations and saccades. In 2013 International Conference on Biometrics, pages 1–8. IEEE, 2013.
  • [31] Corey D Holland and Oleg V Komogortsev. Complex eye movement pattern biometrics: the effects of environment and stimulus. IEEE Transactions on Information Forensics and Security, 8(12):2115–2126, 2013.
  • [32] Corey D Holland and Oleg V Komogortsev. Software framework for an ocular biometric system. In Proceedings of the Symposium on Eye Tracking Research and Applications, pages 365–366. ACM, 2014.
  • [33] Edward Wyndham Hulme et al. Statistical bibliography in relation to the growth of modern civilization. 1923.
  • [34] Inderscience. International journal of biometrics. https://www.inderscience.com/jhome.php?jcode=ijbm, 2019. Access: 01/01/2019.
  • [35] Martti Juhola, Youming Zhang, and Jyrki Rasku. Biometric verification of a subject through eye movements. Computers in biology and medicine, 43(1):42–50, 2013.
  • [36] Adrian Kapczynski, Paweł Kasprowski, and Piotr Kuzniacki. Modern access control based on eye movement analysis and keystroke dynamics. In Proceedings of the International Multiconference on Computer Science and Information Technology, pages 477–483, 2006.
  • [37] P. Kasprowski and K. Harezlak. The second eye movements verification and identification competition. In IEEE International Joint Conference on Biometrics, pages 1–6, Sep. 2014.
  • [38] P Kasprowski and I Rigas. The influence of dataset quality on the results of behavioral biometric experiments. ieee biosig 2013 conference. Lecture Notes in Informatics (LNI), 212.
  • [39] Paweł Kasprowski. The impact of temporal proximity between samples on eye movement biometric identification. In IFIP International Conference on Computer Information Systems and Industrial Management, pages 77–87. Springer, 2013.
  • [40] Pawel Kasprowski and Katarzyna Harezlak. The second eye movements verification and identification competition. In Biometrics (IJCB), 2014 IEEE International Joint Conference on, pages 1–6. IEEE, 2014.
  • [41] Pawel Kasprowski and Katarzyna Harezlak. Disk space and load time requirements for eye movement biometric databases. In AIP Conference Proceedings, volume 1738, page 180007. AIP Publishing, 2016.
  • [42] Pawel Kasprowski and Katarzyna Harezlak. Using dissimilarity matrix for eye movement biometrics with a jumping point experiment. In Intelligent Decision Technologies 2016, pages 83–93. Springer, 2016.
  • [43] Pawel Kasprowski and Katarzyna Harezlak. Fusion of eye movement and mouse dynamics for reliable behavioral biometrics. Pattern Analysis and Applications, 21(1):91–103, 2018.
  • [44] Paweł Kasprowski, Oleg V Komogortsev, and Alex Karpov. First eye movement verification and identification competition at btas 2012. In Biometrics: Theory, Applications and Systems (BTAS), 2012 IEEE Fifth International Conference on, pages 195–202. IEEE, 2012.
  • [45] Pawel Kasprowski and Jozef Ober. Eye movements in biometrics. In International Workshop on Biometric Authentication, pages 248–258. Springer, 2004.
  • [46] Pawel Kasprowski and Józef Ober. With the flick of an eye. Biometric Technology Today, 12(3):7–8, 2004.
  • [47] Pawel Kasprowski and Józef Ober. Enhancing eye-movement-based biometric identification method by using voting classifiers. In Biometric Technology for Human Identification II, volume 5779, pages 314–324. International Society for Optics and Photonics, 2005.
  • [48] Pawel Kasprowski and Ioannis Rigas. The influence of dataset quality on the results of behavioral biometric experiments. In 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG), pages 1–8. IEEE, 2013.
  • [49] Tomi Kinnunen, Filip Sedlak, and Roman Bednarik. Towards task-independent person authentication using eye movement signals. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pages 187–190. ACM, 2010.
  • [50] Alexander Klein, Lori-Ann R Sacrey, Ian Q Whishaw, and Stephen B Dunnett. The use of rodent skilled reaching as a translational model for investigating brain damage and disease. Neuroscience & Biobehavioral Reviews, 36(3):1030–1042, 2012.
  • [51] Oleg Komogortsev, Corey Holland, Sampath Jayarathna, and Alex Karpov. 2d linear oculomotor plant mathematical model: Verification and biometric applications. ACM Transactions on Applied Perception (TAP), 10(4):27, 2013.
  • [52] Oleg Komogortsev, Corey Holland, Alex Karpov, and Larry R Price. Biometrics via oculomotor plant characteristics: Impact of parameters in oculomotor plant model. ACM Transactions on Applied Perception (TAP), 11(4):20, 2015.
  • [53] Oleg Komogortsev, Alexey Karpov, and Corey Holland. Oculomotor plant characteristics: The effects of environment and stimulus. IEEE Transactions on Information Forensics and Security, 11(3):621–632, 2016.
  • [54] Oleg V Komogortsev and Corey D Holland. Biometric authentication via complex oculomotor behavior. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 1–8. IEEE, 2013.
  • [55] Oleg V Komogortsev and Corey D Holland. The application of eye movement biometrics in the automated detection of mild traumatic brain injury. In Proceedings of the extended abstracts of the 32nd annual ACM conference on Human factors in computing systems, pages 1711–1716. ACM, 2014.
  • [56] Oleg V Komogortsev, Corey D Holland, and Alex Karpov. Template aging in eye movement-driven biometrics. In Biometric and Surveillance Technology for Human and Activity Identification XI, volume 9075, page 90750A. International Society for Optics and Photonics, 2014.
  • [57] Oleg V Komogortsev, Sampath Jayarathna, Cecilia R Aragon, and Mechehoul Mahmoud. Biometric identification via an oculomotor plant mathematical model. In Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pages 57–60. ACM, 2010.
  • [58] Oleg V Komogortsev and Alex Karpov. Liveness detection via oculomotor plant characteristics: Attack of mechanical replicas. In 2013 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2013.
  • [59] Oleg V Komogortsev, Alex Karpov, Larry R Price, and Cecilia Aragon. Biometric authentication via oculomotor plant characteristics. In 2012 5th IAPR International Conference on Biometrics (ICB), pages 413–420. IEEE, 2012.
  • [60] Oleg V Komogortsev, Alexey Karpov, and Corey D Holland. Cue: counterfeit-resistant usable eye movement-based authentication via oculomotor plant characteristics and complex eye movement patterns. In Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX, volume 8371, page 83711X. International Society for Optics and Photonics, 2012.
  • [61] Oleg V Komogortsev, Alexey Karpov, and Corey D Holland. Attack of mechanical replicas: Liveness detection with eye movements. IEEE Transactions on Information Forensics and Security, 10(4):716–725, 2015.
  • [62] Oleg V Komogortsev, Alexey Karpov, Corey D Holland, and Hugo P Proença. Multimodal ocular biometrics approach: A feasibility study. In 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pages 209–216. IEEE, 2012.
  • [63] Oleg V Komogortsev and Ioannis Rigas. Bioeye 2015: Competition on biometrics via eye movements. In Biometrics Theory, Applications and Systems (BTAS), 2015 IEEE 7th International Conference on, pages 1–8. IEEE, 2015.
  • [64] Stephen Majebi Lawani. Bibliometrics: its theoretical foundations, methods and applications. Libri, 31(1):294–315, 1981.
  • [65] Dillon Lohr, Samuel-Hunter Berndt, and Oleg Komogortsev. An implementation of eye movement-driven biometrics in virtual reality. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, page 98. ACM, 2018.
  • [66] Zhuo Ma, Xinglong Wang, Ruijie Ma, Zhuzhu Wang, and Jianfeng Ma. Integrating gaze tracking and head-motion prediction for mobile device authentication: A proof of concept. Sensors, 18(9):2894, 2018.
  • [67] Ishan Nigam, Mayank Vatsa, and Richa Singh. Ocular biometrics: A survey of modalities and fusion approaches. Information Fusion, 26:1–35, 2015.
  • [68] Koichiro Niinuma, Unsang Park, and Anil K Jain. Soft biometric traits for continuous user authentication. IEEE Transactions on information forensics and security, 5(4):771–780, 2010.
  • [69] Keyurkumar Patel, Hu Han, and Anil K Jain. Cross-database face antispoofing with robust feature representation. In Chinese Conference on Biometric Recognition, pages 611–619. Springer, 2016.
  • [70] Alan Pritchard et al. Statistical bibliography or bibliometrics. Journal of documentation, 25(4):348–349, 1969.
  • [71] Allyson Rice, P Jonathon Phillips, Vaidehi Natu, Xiaobo An, and Alice J O’Toole. Unaware person recognition from the body when face identification fails. Psychological Science, 24(11):2235–2243, 2013.
  • [72] Ioannis Rigas, Evgeniy Abdulin, and Oleg Komogortsev. Towards a multi-source fusion approach for eye movement-driven recognition. Information Fusion, 32:13–25, 2016.
  • [73] Ioannis Rigas, George Economou, and Spiros Fotopoulos. Biometric identification based on the eye movements and graph matching techniques. Pattern Recognition Letters, 33(6):786–792, 2012.
  • [74] Ioannis Rigas, George Economou, and Spiros Fotopoulos. Human eye movements as a trait for biometrical identification. In Biometrics: Theory, Applications and Systems (BTAS), 2012 IEEE Fifth International Conference on, pages 217–222. IEEE, 2012.
  • [75]

    Ioannis Rigas, Lee Friedman, and Oleg Komogortsev. Study of an extensive set of eye movement features: Extraction methods and statistical analysis.

    Journal of Eye Movement Research, 11(1):3, 2018.
  • [76] Ioannis Rigas, Oleg Komogortsev, and Reza Shadmehr. Biometric recognition via eye movements: Saccadic vigor and acceleration cues. ACM Transactions on Applied Perception (TAP), 13(2):6, 2016.
  • [77] Ioannis Rigas and Oleg V Komogortsev. Biometric recognition via fixation density maps. In Biometric and Surveillance Technology for Human and Activity Identification XI, volume 9075, page 90750M. International Society for Optics and Photonics, 2014.
  • [78] Ioannis Rigas and Oleg V Komogortsev. Biometric recognition via probabilistic spatial projection of eye movement trajectories in dynamic visual environments. IEEE Transactions on Information Forensics and Security, 9(10):1743–1754, 2014.
  • [79] Ioannis Rigas and Oleg V Komogortsev. Gaze estimation as a framework for iris liveness detection. In IEEE International Joint Conference on Biometrics, pages 1–8. IEEE, 2014.
  • [80] Ioannis Rigas and Oleg V Komogortsev. Eye movement-driven defense against iris print-attacks. Pattern Recognition Letters, 68:316–326, 2015.
  • [81] Ioannis Rigas and Oleg V Komogortsev. Current research in eye movement biometrics: An analysis based on bioeye 2015 competition. Image and Vision Computing, 58:129–141, 2017.
  • [82]

    Usman Saeed. Automatic person recognition using eye movement during scene understanding. In

    17th IEEE International Multi Topic Conference 2014, pages 240–244. IEEE, 2014.
  • [83] Usman Saeed. A survey of automatic person recognition using eye movements. International Journal of Pattern Recognition and Artificial Intelligence, 28(08):1456015, 2014.
  • [84] Usman Saeed. Eye movements during scene understanding for biometric identification. Pattern Recognition Letters, 82:190–195, 2016.
  • [85] Ivo Sluganovic, Marc Roeschlin, Kasper B Rasmussen, and Ivan Martinovic. Using reflexive eye movements for fast challenge-response authentication. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pages 1056–1067, 2016.
  • [86] Ivo Sluganovic, Marc Roeschlin, Kasper B Rasmussen, and Ivan Martinovic. Analysis of reflexive eye movements for fast replay-resistant biometric authentication. ACM Transactions on Privacy and Security (TOPS), 22(1):1–30, 2018.
  • [87] IEEE Signal Processing Society. Ieee transactions on information forensics and security, 2019.
  • [88] SPIE. Proceedings of spie the international society for optical engineering. https://spie.org/publications/conference-proceedings, 2019. Access: 01/01/2019.
  • [89] Springer. Advances in intelligent systems and computing. https://www.springer.com/series/11156, 2019. Access: 01/01/2019.
  • [90] Springer. Lecture notes in computer science. https://www.springer.com/series/558, 2019. Access: 05/01/2019.
  • [91] Shiliang Sun. Multitask learning for eeg-based biometrics. In 2008 19th international conference on pattern recognition, pages 1–4. IEEE, 2008.
  • [92] Darius Vitonis and Dan Witzner Hansen. Person identification using eye movements and post saccadic oscillations. In 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, pages 580–583. IEEE, 2014.
  • [93] Ran Wang, Jing Xiao, Ruimin Hu, and Xu Wang. Face anti-spoofing based on motion. In Pacific Rim Conference on Multimedia, pages 202–211. Springer, 2017.
  • [94] Xiaomeng Wang, Kang Liu, and Xu Qian. A survey on gaze estimation. In

    2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

    , pages 260–267. IEEE, 2015.
  • [95] Yunxin Wang, Tiegen Liu, and Li Liu. Novel algorithm for iris localization. In MIPPR 2007: Pattern Recognition and Computer Vision, volume 6788, page 67880L. International Society for Optics and Photonics, 2007.
  • [96] Erjia Yan, Ying Ding, and Qinghua Zhu. Mapping library and information science in china: A coauthorship network analysis. Scientometrics, 83(1):115–131, 2010.
  • [97]

    Raimondas Zemblys, Diederick C Niehorster, Oleg Komogortsev, and Kenneth Holmqvist. Using machine learning to detect events in eye-tracking data.

    Behavior research methods, 50(1):160–181, 2018.
  • [98] Youming Zhang and Martti Juhola. On applying signals of saccade eye movements for biometric verification of a subject. In MDA, pages 78–92, 2013.
  • [99] Youming Zhang and Martti Juhola. Biometric verification of a user based on eye movements. International Journal of Biometrics, 6(2):106–124, 2014.
  • [100] Youming Zhang and Martti Juhola. On biometrics with eye movements. IEEE journal of biomedical and health informatics, 21(5):1360–1366, 2017.
  • [101] Youming Zhang, Jorma Laurikkala, and Martti Juhola. Biometric verification of a subject with eye movements, with special reference to temporal variability in saccades between a subject’s measurements. International Journal of Biometrics, 6(1):75–94, 2014.
  • [102] Youming Zhang, Jorma Laurikkala, and Martti Juhola. Biometric verification with eye movements: results from a long-term recording series. IET Biometrics, 4(3):162–168, 2015.
  • [103] Youming Zhang, Jyrki Rasku, and Martti Juhola. Biometric verification of subjects using saccade eye movements. International Journal of Biometrics, 4(4):317–337, 2012.