Age group and gender recognition from human facial images

03/29/2013 ∙ by Tizita Nesibu Shewaye, et al. ∙ 0

This work presents an automatic human gender and age group recognition system based on human facial images. It makes an extensive experiment with row pixel intensity valued features and Discrete Cosine Transform (DCT) coefficient features with Principal Component Analysis and k-Nearest Neighbor classification to identify the best recognition approach. The final results show approaches using DCT coefficient outperform their counter parts resulting in a 99 rate (considering four distinct age groups) in unseen test images. Detailed experimental settings and obtained results are clearly presented and explained in this report.

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References

  • [1] G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.
  • [2] X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation. In Proceedings of the 14th annual ACM international conference on Multimedia, MULTIMEDIA ’06, pages 307–316, New York, NY, USA, 2006. ACM.
  • [3] E. Mekinen and R. Raisamo. Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30:541–547, 2008.
  • [4] H. Moon and P. J. Phillips. Computational and performance aspects of PCA-based face-recognition algorithms. Perception, 30(3):303–321, 2001.
  • [5] C. Netzer and P. Srinivasan. Facial image database for gender recognition. http://scien.stanford.edu/pages/labsite/2001/ee368/ projects2001/dropbox/project16/appendix.html,accessed on April 09, 2012.
  • [6] C. B. Ng, Y. H. Tay, and B.-M. Goi. Vision-based human gender recognition: A survey. CoRR, abs/1204.1611, 2012.
  • [7] B.-C. Shen, C.-S. Chen, and H.-H. Hsu. Fast gender recognition by using a shared-integral-image approach. In Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’09, pages 521–524, Washington, DC, USA, 2009. IEEE Computer Society.
  • [8] O. Smirg, J. Mikulka, M. Faundez-Zanuy, M. Grassi, and J. Mekyska. Gender recognition using pca and dct of face images. In J. Cabestany, I. Rojas, and G. Joya, editors, Advances in Computational Intelligence, volume 6692 of Lecture Notes in Computer Science, pages 220–227. Springer Berlin / Heidelberg, 2011.
  • [9] K. Ueki, T. Hayashida, and T. Kobayashi. Subspace-based age-group classification using facial images under various lighting conditions. In Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, FGR ’06, pages 43–48, Washington, DC, USA, 2006. IEEE Computer Society.
  • [10] P. A. Viola and M. J. Jones. Robust real-time face detection. In International Conference on Computer Vision, page 747, 2001.
  • [11] Wikipedia Article. K-nearest neighbour. http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm, accessed on May 15, 2012.
  • [12] Wikipedia Article. Principal component analysis (pca). http://en.wikipedia.org/wiki/Principal_component_analysis, accessed on May 15, 2012.