Haruyuki Iwama

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

This paper describes the worlds 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.

Gait Verification System for Criminal Investigation

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.

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

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.

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

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.

Gait-based person-verification system for forensics

The first gait-based person-verification system that can analyze the gait for forensic science is presented. There are many security cameras set in many places, and gait image sequences can be extracted from the images taken by the cameras. The gait can provide information for identity determination in forensic science; however, the limitation of gait-based person authentication is that the analysis of gait features is a difficult task for non-specialists of gait person authentication. Our system allows non-specialists to analyze gait images taken in different circumstances, and can provide useful information for criminal investigations. We performed evaluation experiments to evaluate the usability of the constructed system. In the experiments, nine participants used the system to analyze gait features. The experimental results show that novices can use the constructed system correctly and obtain reasonable results.

Multi-view multi-modal person authentication from a single walking image sequence

This paper describes a method for multi-view multimodal biometrics from a single walking image sequence. As multi-modal cues, we adopt not only face and gait but also the actual height of a person, all of which are simultaneously captured by a single camera. As multi-view cues, we use the variation in the observation views included in a single image sequence captured by a camera with a relatively wide field of view. This enables us to improve the authentication of a person based on multiple modalities and views, while retaining the potential for real applications such as surveillance and forensics using only a single image sequence (a single session with a single camera). In the experiments, we constructed a large-scale multi-view multimodal score data set with 1,912 subjects, and evaluated the proposed method using the data set in a statistically reliable way. We achieved 0.37% equal error rates for the false acceptance and rejection rates in the verification scenarios, and 99.15% rank-1 identification rate in the identification scenarios.

Foreground and shadow segmentation based on a homography-correspondence pair

A static binocular camera system is widely used in many computer vision applications; and being able to segment foreground, shadow, and background is an important problem for them. In this paper, we propose a homography-correspondence pair-based segmentation framework. Existing segmentation approaches, based on homography constraints, often suffer from occlusion problems. In our approach, we treat a homography-correspondence pair symmetrically, to explicitly take the occlusion relationship into account, and we regard the segmentation problem as a multi-labeling problem for the homography-correspondence pair. We then formulate an energy function for this problem and get the pair-wise segmentation results by minimizing them via an α-β swap algorithm. Experimental results show that accurate segmentation is obtained in the presence of the occlusion region in each side image.

Score-level fusion by generalized Delaunay triangulation

This paper describes a method for score-level fusion in multi-cue two-class classification problems. Fusion based on the probability density function (PDF) of multiple scores given for each class is a promising approach because it guarantees optimality as long as the estimated PDFs are correct. Instead of lattice-type control points used in previous non-parametric density-based approaches, floating control points (FCPs) are introduced to improve scalability and the whole posterior distribution is represented by interpolation or extrapolation using generalized Delaunay triangulation. Given a set of FCPs obtained by k-means, posteriors on the FCPs are estimated by an energy minimization framework using training samples. The experiments, using both simulation data as well as several types of real data from three publicly available score databases for multi-cue biometric authentication, demonstrate the effectiveness of the proposed method.

On combining gait features

This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.

Gait Verification System for Supporting Criminal Investigation

We constructed gait verification system for criminal investigation. The system is designed so that criminal investigators can use it and obtain professional gait verification results. We think the system can support criminal investigation where gait can be a clue of the perpetrators identity. We summarize the constructed system in this paper.