This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.

A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

This paper describes the world's largest gait database-the “OU-ISIR Gait Database, Large Population Dataset”-and its application to a statistically reliable performance evaluation of vision-based gait recognition Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years The dataset allows us to determine statistically significant performance differences between currently proposed gait features In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth

/pdf/the-ou-isir-gait-database-comprising-the-large-population-3ra12v92eg.pdf

The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition

This paper proposes a method of gait recognition using a convolutional neural network (CNN). Inspired by the great successes of CNNs in image recognition tasks, we feed in the most prevalent image-based gait representation, that is, the gait energy image (GEI), as an input to a CNN designed for gait recognition called GEINet. More specifically, GEINet is composed of two sequential triplets of convolution, pooling, and normalization layers, and two subsequent fully connected layers, which output a set of similarities to individual training subjects. We conducted experiments to demonstrate the effectiveness of the proposed method in terms of cross-view gait recognition in both cooperative and uncooperative settings using the OU-ISIR large population dataset. As a result, we confirmed that the proposed method significantly outperformed state-of-the-art approaches, in particular in verification scenarios.

GEINet: View-invariant gait recognition using a convolutional neural network

This paper describes the world’s largest gait database with wide view variation, the “OU-ISIR gait database, multi-view large population dataset (OU-MVLP)”, and its application to a statistically reliable performance evaluation of vision-based cross-view gait recognition. Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0° −90°, 180° −270°. In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods.

/pdf/multi-view-large-population-gait-dataset-and-its-performance-1l00ylsahj.pdf

Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition

In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.

Deep Learning for Biometrics: A Survey

This paper describes the first gait verification system for criminal investigation using footages from surveillance cameras. The system is designed so that the criminal investigators as non-specialists on computer vision-based gait verification can, independently, use it to verify unknown perpetrators as suspects or ex-convicts in criminal investigations. Each step of the gait verification process is proceeded by interactive operation on a graphics-user interface. Eventually, for each pair of compared subjects selected by a user, the system outputs a posterior probability on a verification result, which indicates that compared subjects are the same, with the consideration of various circumstances of the subjects such as the size, frame-rate, observation views, and clothing of subjects. One gait-specialist and ten nongait-specialists participated in operation tests of the system using five different datasets with various types of scenes, each of which contained two or three verification sets. It was shown that all the non-gait-specialists, as well as the gait-specialist, could obtain reasonable verification results for almost all of the verification sets.

https://www.jstage.jst.go.jp/article/imt/8/4/8_1187/_pdf

Gait Verification System for Criminal Investigation

This paper addresses gait-based age estimation using a large-scale whole-generation gait database Previous work on gait-based age estimation evaluated their methods using databases that included only 170 subjects at most with a limited age variation, which was insufficient to statistically demonstrate the possibility of gait-based age estimation Therefore, we first constructed a much larger whole-generation gait database which includes 1,728 subjects with ages ranging from 2 to 94 years We then provided a baseline algorithm for gait-based age estimation implemented by Gaussian process regression, which has achieved successes in the face-based age estimation field, in conjunction with silhouette-based gait features such as an averaged silhouette (or Gait Energy Image) which has been used extensively in many gait recognition algorithms Finally, experiments using the whole-generation gait database demonstrated the viability of gait-based age estimation

/pdf/gait-based-age-estimation-using-a-whole-generation-gait-55y01iqiq7.pdf

Gait-based age estimation using a whole-generation gait database

This paper describes a method for person reidentification across multiple non-overlapping cameras using both gait and color features. Because a single color feature is insufficient to distinguish persons with similar color clothes, a spatio-temporal histogram of oriented gradients is employed as a gradient-based shape and motion gait feature to discriminate such persons in conjunction with a background edge attenuation technique. However, since the gait feature is more sensitive to view differences than the color feature, a view-dependent score-level fusion framework adaptively controls the weights of the gait and color features. Experiments across seven non-overlapping cameras confirm the effectiveness of the proposed method.

/pdf/person-re-identification-using-view-dependent-score-level-1ffpcjlguk.pdf

Person re-identification using view-dependent score-level fusion of gait and color features

This paper describes the construction of the largest gait database in the world and its application to a statistically reliable performance evaluation of vision-based gait recognition. Whereas existing gait databases include at most an order of a hundred subjects, we construct an even larger gait database which includes 1,035 subjects (569 males and 466 females) with ages ranging from 2 to 94 years. Because a sufficient number of subjects for each gender and age group are included in this very large-scale gait database, we can analyze the dependence of gait recognition performance on gender or age groups. The results of GEI-based gait recognition provide several novel insights, such as the tradeoff of gait recognition performance among age groups derived from the maturity of walking ability and physical strength. Moreover, improvement in the statistical reliability of performance evaluation is shown by comparing the gait recognition results for the whole set and subsets of a hundred subjects selected randomly from the whole set.

/pdf/performance-evaluation-of-vision-based-gait-recognition-5675u3x4j4.pdf

Haruyuki Iwama

Papers

The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition

Gait Verification System for Criminal Investigation

Gait-based age estimation using a whole-generation gait database

Person re-identification using view-dependent score-level fusion of gait and color features

Performance evaluation of vision-based gait recognition using a very large-scale gait database