Nowadays, videos have dominated the flow on internet. Compared to still images, videos enrich the content by supplying audio and temporal information. As a result, video understanding is of urgent demand for practical usage, and person identification in videos is one of the most important tasks. Person identification has been widely studied in different research areas, including face recognition, person re-identification, and speaker recognition. In the context of video analysis, each research topic addresses a single modal of information. As deep learning arises in recent years, all these techniques have achieved great success. In the area of face recognition, ArcFace  reached a precision of 99.83% on the LFW benchmark , which had surpass the human performance. The best results on Megaface  has also reached 99.39%. For person re-identification (Re-ID), Wang et al.  raised the Rank-1 accuracy of Re-ID to be 97.1% on the Market-1501 benchmark . In the field of speaker recognition, the Classification Error Rates of SincNet  on the TIMIT dataset  and LibriSpeech dataset  are merely 0.85% and 0.96%, respectively.
|YouTube Celebrities ||Recognition||47||Video||1,910|
|YouTube Faces ||Recognition||1,595||Video||3,425|
Everything seems alright until we try to apply these person identification methods to the real unconstrained videos. Face recognition is sensitive to pose, blur, occlusion, etc. Moreover, in many video frames, the faces are even invisible. Re-ID has not touched the problem of changing clothes yet. For speaker recognition, one major challenge comes from the fact that the person to recognize is not always speaking. Generally speaking, every single technique is inadequate to cover all the cases. Intuitively, it will be beneficial to combine all these sub-tasks together, so we can fully utilize the rich content of videos.
There are several datasets for person identification in the literature. We list the popular datasets in Table 1. Most of the video datasets focus on only one modal of feature, either face [25, 74] or full body [71, 81, 21]. To our best knowledge there is no large-scale dataset that addresses the problem of multi-modal person identification.
Here we present the iQIYI-VID dataset, which is the first video dataset for multi-model person identification. It contains 600K video clips of 5,000 celebrities, which is the largest celebrity identification dataset. All the videos are manually labeled, which makes it a good benchmark to evaluate person identification algorithms. This dataset aims to encourage the development of multi-modal person identification.
In Section 2, we will review related works of person identification briefly. We present the iQIYI-VID dataset in Section 3. Our baseline method will be given in Section 4. In Section 5, we evaluate our baseline and related methods on our dataset. We conclude our work in Section 6.
2 Related Work
2.1 Face Recognition
Typically, face recognition consists of two tasks, face verification and face identification. Face verification is to determine whether two given face images belong to the same person. In 2007, the Labeled Faces in the Wild (LFW) dataset was built for face verification by Huang et al. . The LFW database includes 13K photos of 5K different people, and it is the most widely used benchmark for verification. After that, a large number of algorithms were proposed to solve the problem of face verification, and many of them [50, 56, 58, 59, 60] have reached recognition rates of over 99.9% on LFW, which is better than human performance .
In recent years, the interest in face identification has greatly increased. The task of face identification is to find the most similar faces between gallery set and query set. Each individual in gallery set only has some typical face images (less than five). Currently, the most famous and difficult benchmarks are Megaface  and MS-Celeb-1M database . Megaface includes one million unconstrained and multi-scaled photos of ordinary people collected from Flickr . The MS-Celeb-1M database provides one million face images of celebrities selected from freebase with corresponding entity keys. Both datasets are large enough for training. However, contents in these two datasets are still images. They are not suitable for training models that recognize faces in unconstrained videos. Moreover, these datasets are noisy as pointed out by . In 2008, Kim et al. created YouTube celebrity recognition database  that includes only 35 celebrities, mostly actors/actresses and politicians, from YouTube. The Youtube Face Database(YFD)  contains 3425 videos of 1595 different people. Both databases have much less videos than iQIYI-VID. Another difference is that our database includes video clips without visible faces.
With the successful application of deep learning in computer vision, the algorithms based on convolutional neural network (CNN) have gradually become the mainstream of face recognition. Since 2014, Yi Sunet al. proposed DeepID series [56, 58, 57, 55] based on contrastive loss. In particular, DeepID3 
added joint face identification-verification supervision to both intermediate and final feature extraction layers during training, which achieved an accuracy of 99.53% on the LFW dataset. FaceNet adopted the triplet loss. It generated triples that directly learnt a mapping from face images to a compact Euclidean space, where distances directly corresponded to a measure of face similarity. An alternative is the center loss , which makes each class more compact in the feature space. The latest face recognition algorithms [33, 32, 34, 67, 9] focused on improving angular/cosine margin-based loss, which made learned features potentially separable with a larger angular/cosine distance. The state-of-art method, ArcFace  achieved a face verification accuracy of 99.83% on LFW. In this paper we take ArcFace to recognize faces in videos.
Face detection is a fundamental step for face recognition. Thanks to the development of deep learning, face detection has achieved great successes in recent years. Recent CNN-based face detectors can be categorized into single-stage detectors [20, 76, 13, 35, 78, 70, 42, 61] and two-stage detectors [69, 83, 29, 72]. There are also cascaded detectors [27, 77] that achieve a very fast speed. We choose SSH  as the basic detector, which is the best open-source face detector at this time.
2.2 Audio-based Speaker Identification
Speaker recognition has been an active research topic for many decades . It comes in two forms that speaker verification  aims to verify if two audio samples belong to the same speaker, whereas speaker identification 
predicts the identity of the speaker given an audio sample. Speaker recognition falls into two categories, text dependent  and text independent . Speaker recognition has been approached by applying a variety of machine learning models[3, 4, 10], either standard or specifically designed, to speech features such as MFCC . For many years, the GMM-UBM framework 
dominated this particular field. Recently i-vector and some related methodologies [24, 7, 23] emerged and became increasingly popular. More recently, d-vector based on deep learning [26, 15, 63] have achieved competitive results against earlier approaches on some datasets. Nevertheless, speaker recognition still remains a challenge, especially for data collected in uncontrolled environment and from heterogeneous sources.
Currently, there are not many freely available datasets for speaker recognition, especially for large-scale ones. National Institute of Standards in Technology (NIST) have hosted several speaker recognition evaluations. However, the associated datasets are not freely available. There are datasets originally intended for speech recognition, such as TIMIT  and LibriSpeech , which have been used for speaker recognition experiments. Many of these datasets were collected under controlled conditions and therefore are improper for evaluating models in real-world conditions. To fill the gap, the Speaker in the Wild (SITW) dataset  were created from open multi-media resources and freely available to the research community. To the best of our knowledge, the largest, freely available speaker recognition datasets are VoxCeleb  and VoxCeleb2 . They were collected using a completely automatic pipeline and therefore could scale to thousands of speakers. More importantly, similar to SITW datset, the VoxCeleb datasets were created from YouTube videos, ensuring their data diversity in terms of data quality and background noise.
2.3 Body Feature Based Person Re-Identification
Person re-identification (Re-ID) recognizes people across cameras, which is suitable for videos that have multiple shots switching between cameras. In recent years, Re-ID has made a lot of breakthroughs. For single-frame-based methods, the model mainly extracts features from a still image, and directly determines whether the two pictures belong to the same person [11, 30]. In order to improve the generalization ability, character attributes of the person was added to the network 
. Metric learning measures the degree of similarity by calculating the distance between pictures, focusing on the design of the loss function[64, 51, 16]. These methods rely on the global feature matching on the whole image, which is sensitive to the background. To solve this problem, local features gradually arises. Various strategies are proposed to extract local features, including image dicing, skeleton point positioning, and attitude calibration [65, 80, 79]. Image dicing has the problem of data alignment . Therefore, some researchers proposed to extract the pose information of the human body through skeleton point detection and introduce STN for correction . Zhang et al.  further calculated the shortest path between local features and introducing a mutual learning approach, so that the accuracy of recognition exceeds that of humans for the first time.
The information within a single frame is often limited. In comparison, video sequence-based methods can extract temporal information to aid Re-ID. AMOC  adopted two sub-networks to extract the features of global content and motion. Song et al.  adopted self-learning to find out low-quality image frames and reduce their importance, which made the features more representative.
Existing video-based Re-ID datasets, such as DukeMTMC , iLIDS  and MARS , have invariant scenes and small amounts of data. The full body are usually visible, and the clothes are always unchanged for the same person crossing cameras. In comparison, video clips of iQIYI-VID are extracted from the massive video database of iQIYI, which covers various types of scenes and the same id spans multiple kinds of videos. Significant changes in character appearances make Re-ID more challenging but closer to practical usage. The image resolution is also much higher than the other datasets.
3 iQIYI-VID DATASET
The iQIYI-VID dataset is a large-scale benchmark for multi-modal person identification. To make the benchmark close to real applications, we extract video clips from real online videos of extensive types. Then we label all the clips by human annotators. Automatic algorithms are adopted to accelerate the collection and labeling process. The flow chart of the construction process is shown in Figure 1. We begin with extracting video clips from a huge database of long videos. Then we filter out those clips with no person or multiple persons by automatic algorithms. After that we group the candidate clips by identity and put them into manual annoation. The details are given below.
3.1 Extracting video clips
The raw videos are the top 500,000 popular videos of iQIYI, covering movies, teleplays, variety shows, news broadcasting, and so on. Each raw video is segmented into shots according to the dissimilarity between consecutive frames. Video clips that are shorter than one second are excluded from the dataset, since they often contain not enough multi-modal information. Those clips longer than 30 seconds are also dropped out due to a large computation burden.
3.2 Automatic filtering by head detection
As a benchmark for person identification, each video clip is required to contain one and only one major character. In order to find out the major characters, heads are detected by YOLO V2 . A valid frame is defined as a frame in which only one head is detected, or the biggest head is three times larger than the other heads. A valid clip is defined as a clip whose valid frames exceed a ratio of 30%. Invalid clips are dropped out. Since the head detector cannot detect all the heads, some clips will survive in this stage. Such noise clips will be thrown away in the manual filtering step.
3.3 Obtaining candidate clips for each identity
In this stage, each clip will be labeled with an initial identity by face recognition or clothes clustering. All the identities are selected from the celebrity database of iQIYI. The faces are detected by the SSH model , and then recognized by the ArcFace model . After that, the clothes and head information are used to cover those clips that no face has been detected or recognized. We cluster the clips from the same video by the faces and clothes information. The face and clothes in each frame are paired according to their relative position, and the identities of faces are propagated to the clothes with the face-clothes pairs. After that, each clothes cluster can get an ID through majority voting. The clips falling into the same cluster are regarded to be with the same identity, so the clips with no recognizable face can inherit the label from their clusters. Note that, the bounding boxes of faces and clothes are not included in the published dataset, which matches the case in real applications.
3.4 Final manual filtering
At this point, we have obtained all the candidate clip clusters with noise. All clusters of clips are cleaned by a manual annotation process. More labeling efforts are put on the clips with low scores in face recognition and those been extracted by clothes clustering.
The manual labeling was repeated twice by different labelers to ensure a high quality. After data cleaning, we randomly selected 10% of the dataset for quality testing and the data labeling error rate was kept within 0.2%.
IQIYI-VID dataset contains 600K videos, which is much larger than the datasets listed in Table 1. The whole dataset is divided into three parts, 40% for training, 30% for validation, and 30% for test. Researchers can download the training and validation parts from the website 111http://challenge.ai.iqiyi.com/detail?raceId=5afc36639689443e8f815f9e, and the test part is kept secret for evaluating the performance of contesters. The video clips are labeled with 5,000 identities from the iQIYI celebrity database. Among all the celebrities, 4,360 of them are Asian, 510 are Caucasian, 41 are African, and 23 are Hispanic. To mimic the real environment of video understanding, we add 84,759 distracter videos with unknown person identities (outside the 5,000 major identities in training set) into the validation set and another 84,746 distracter videos into the test set.
The number of video clips for each celebrity varies between 20 and 900, with 100 on average. The histogram of the number of video clips over ID is shown in Figure 1(a). As shown in Figure 1(c), the video clip duration is in the range of 1 30 seconds, with 4.72 seconds on average.
The dataset is approximately gender-balanced, with 54% males and 46% females. The age range of the dataset is quite considerable, the earliest birth date was 1877 and the latest birth date was 2012. The histogram is shown in Figure 1(b).
5 Baseline Approaches
In this section we present a baseline approach for multi-modal person identification on the iQIYI-VID dataset. Our video processing flow takes a video as input, extracts multi-modal features on single frames separately, and fuses them into video-level features. Then we train classifiers on these video-level features. Video retrieval is finally realized by ranking test videos according to the probability distribution over the IDs.
5.1 Face recognition
As shown in Figure 3, the whole procedure for face recognition includes three main modules, including face detection, face recognition, and feature fusion.
Face Detection. We use the SSH model  to detect faces, which is the best open-source face detector. To accelerate the detection, we replace the VGG16 backbone network  of the original SSH with MobileNetV1 .
Face Recognition and Quality Assessment. We choose the state-of-art face recognition model ArcFace . We also found that the face quality is crucial for face verification and identification performance in practice. So we train a face quality classification model. The classification model can help us make full use of good faces and suppress the interference of bad faces. In our experiment, the face quality is categorized into four types:
Blurred face: The image of face is blurred. In many cases these face images cannot be detected nor recognized reliably.
Good face: Faces are clear, and face angles are less than 60 degrees.
Medium face: It contains not only acceptable face images, but also includes side faces and partial misdetected faces.
Side face: Face angles are greater than 60 degrees.
Feature Fusion. In the literature there are many aggregation techniques for getting video-level representations from frames, such as Fisher Vectors (FV)  and VLAD . But those techniques are task-independent, which do not consider face quality in this case. In contrast, we take the cluster center of face features as the video-level presentations based on face quality levels. We sort face quality in the order of good face, side face, medium face, and blurred face. Firstly, if a video contains good face, we take the cluster center of good faces as the video-level features. Then if a video does not contain any good face but has side faces, we only take side face into account. The medium faces and blurred faces are examined in turn.
5.2 Multi-modal features
We utilize the state-of-art models to extract multi-modal features, including head, audio, and body. The procedure is similar to face recognition in Section 5.1.
Head Recognition. We train a head classifier based on the ArcFace model . The heads are detected by YOLO V2  as mentioned in Section 3.2. The head features contain information from hair style and accessories, which are good supplement to faces.
. The audio from the video clips is converted to a single-channel, 16-bit signal with a 16kHz sampling rate. The magnitude spectrograms of these audio are mean-subtracted, but variance normalisation is not performed. Instead, we only divide those mean-subtracted spectgrograms by a constant value 50.0 and feed the results as input to a CNN model based on ResNet34, inspired by . The CNN model is trained as a classification model using the dev part of Voxceleb2 dataset  with 5994 speakers, of the data is used as evaluation while the rest as training data. We achieved a best classification error rate of on the evaluation data. The 512D output from the last hidden layer is used as speaker embedding.
5.3 Model fusion
. We extract features from the single models and fuse them simply by a weighted sum. In our implementation, the feature layer right before the softmax layer is fed to the fusion. We set the feature lengths of all the models to be exactly the same, so they can be added up.
6.1 Experiment setup
The modified SSH face detector is trained on the Widerface dataset . The face quality classification model in Section 5.1 is trained on our private dataset. In Section 5.3, the feature length is set to 512. The weights for face, head, audio, and body are empirically set to 1, 0.5, 0.5, and 0.2, respectively. The underlying strategy is that more reliable features gains higher weights.
6.2 Evaluation Metrics
To evaluate the retrieval results, we use Mean Average Precision () :
where is the set of person IDs to retrieve, is the number of positive examples within the top retrieval results for the -th ID, and is the set of ranked retrieval results from the top until you get positive examples. In our implementation, only top 100 retrievals are kept for each person ID.
|Combine 3 models||89.24|
|Combine 6 models||89.35|
6.3 Results on iQIYI-VID
We evaluate the models described in Section 5 on the test set. The results are given in Table 2. Among all the models using single modal of feature, face recognition achieved the best performance. It should be mentioned that, ArcFace  achieved a precision of 99.83% on the LFW benchmark , which is much higher than on the iQIYI-VID dataset. It suggested that, the iQIYI-VID dataset is more challenging than LFW. In Figure 7 we show some difficult examples that are hard to recognize using only face features.
None of the other features alone is comparable to face features. The head feature is unreliable when the face is hidden. In this case the classifier mainly rely on hair or accessories, which are much less discriminative than face features. Moreover, the actors or actresses often change their hairstyles across the shows, as shown in Figure 4.
The MAP of audio-based model is only . The main reason is that the person identity of the video clip is primarily determined by the figure in the video frame, we didn’t use a voice detection module to filter out non-speaking clips, nor was active speaker detection employed. As a result, the sound may likely not come from the person of interest. Moreover, in many cases, even though the person of interest does speak, the voice actually comes from a voice actor for that person, which makes recognition by speaker more problematic. When the character does not say anything inside the clip and the audio may come from the background, or even from some other characters, which are distracters for speaker recognition.
The performance of body feature is even worse. The main challenge comes from two aspects. In the one hand, the clothes of the characters always change from one show to another. In the other hand, the uniforms of different characters may be nearly the same in some shows. Figure 5 gives an example. As a result, the intra-class variation is comparable to, if not larger than, the inter-class variation.
Although neither head, audio, nor body feature alone can recognize person well enough, they can produce better classifiers when combined with the face feature. From Table 2 we can see that, adding more features always achieves better performances. Adopting all four kinds of features can raise the MAP by over , from to . It proves that multi-modal information is important for video-based person identification. An example is shown in Figure 6. We can see that when multi-modal features are added to person recognition gradually, more and more positive examples are retrieved back.
Inspired by the strategy of Cross Validation, we built a simple expert system that is composed of models trained on different partition of the training set. The output of the expert system is the sum of the output probability distribution of all the models. When using three models, the MAP can be raised by further, from to . However, using more experts will not increase the performance much. As shown in Table 2, using six models, the MAP can be raised only , that is, from to .
7 Conclusions and Future Work
In this work, we investigated the challenges of person identification in real videos. We built a large-scale video dataset called iQIYI-VID, which contains more than 600K video clips and 5,000 celebrities extracted from iQIYI copyrighted videos. Our baseline approach of multi-modal person identification demonstrated that it is beneficial to take different sources of features to deal with real-world videos. We hope this new benchmark can promote the research in multi-modal person identification.
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