Abstract book (pdf) - ICPR 2010

09:00-09:20, Paper WeAT7.1
Multi-View Gait Recognition based on Motion Regression using Multilayer Perceptron
Kusakunniran, Worapan, Univ. of New South Wales
Wu, Qiang, Univ. of Tech. Sydney
Zhang, Jian, National ICT Australia
Li, Hongdong, Australian National Univ.
It has been shown that gait is an efficient biometric feature for identifying a person at a distance. However, it is a challenging
problem to obtain reliable gait feature when viewing angle changes because the body appearance can be different under
the various viewing angles. In this paper, the problem above is formulated as a regression problem where a novel View
Transformation Model (VTM) is constructed by adopting Multilayer Perceptron (MLP) as regression tool. It smoothly estimates
gait feature under an unknown viewing angle based on motion information in a well selected Region of Interest
(ROI) under other existing viewing angles. Thus, this proposal can normalize gait features under various viewing angles
into a common viewing angle before gait similarity measurement is carried out. Encouraging experimental results have
been obtained based on widely adopted benchmark database.
09:20-09:40, Paper WeAT7.2
Robust Gait Recognition against Speed Variation
Aqmar, Muhammad Rasyid, Tokyo Inst. of Tech.
Shinoda, Koichi, Tokyo Inst. of Tech.
Furui, Sadaoki
Variations in walking speed have a strong impact on the recognition of gait. We propose a method of recognition of gait
that is robust against walking-speed variations. It is established on a combination of Fisher discriminant analysis (FDA)based
cubic higher-order local auto-correlation (CHLAC) and the statistical framework provided by hidden Markov models
(HMMs). The HMMs in this method identify the phase of each gait even when walking speed changes nonlinearly, and
the CHLAC features capture the within-phase spatio-temporal characteristics of each individual. We compared the performance
of our method with other conventional methods in our evaluation using three different databases, i.e., USH,
USF-NIST, and Tokyo Tech DB. Ours was equal or better than the others when the speed did not change too much, and
was significantly better when the speed varied across and within a gait sequence.
09:40-10:00, Paper WeAT7.3
Gait Recognition using Period-Based Phase Synchronization for Low Frame-Rate Videos
Mori, Atsushi, Osaka Univ.
Makihara, Yasushi, The Inst. of Scientific and Industrial Res. Univ.
Yagi, Yasushi, Osaka Univ.
This paper proposes a method for period-based gait trajectory matching in the eigenspace using phase synchronization for
low frame-rate videos. First, a gait period is detected by maximizing the normalized autocorrelation of the gait silhouette
sequence for the temporal axis. Next, a gait silhouette sequence is expressed as a trajectory in the eigenspace and the gait
phase is synchronized by time stretching and time shifting of the trajectory based on the detected period. In addition, multiple
period-based matching results are integrated via statistical procedures for more robust matching in the presence of
fluctuations among gait sequences. Results of experiments conducted with 185 subjects to evaluate the performance of
the gait verification with various spatial and temporal resolutions, demonstrate the effectiveness of the proposed method.
10:00-10:20, Paper WeAT7.4
Body Motion Analysis for Multi-Modal Identity Verification
Williams, George, NYU
Taylor, Graham, NYU
Smolskiy, Kirill, NYU
Bregler, Christoph, NYU
This paper shows how Body Motion Signature Analysis a new soft-biometrics technique can be used for identity verification.
It is able to extract motion features from the upper body of people and estimates so called super-features for input
to a classifier. We demonstrate how this new technique can be used to identify people just based on their motion, or it can
be used to significantly improve hard-biometrics techniques. For example, face verification achieves on this domain 6.45%
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