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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,...
We constructed a large-scale multi-quality multi-modal biometric score database to advance studies on quality-dependent score-level fusion. In particular, we focused on single sensor-based multi-modal biometrics because of their advantages of simple system construction, low cost, and wide availability in real situations such as CCTV footage-based criminal investigation, unlike conventional individual...
Gait is a unique and promising behavioral biometrics which allows to authenticate a person even at a distance from the camera. Since a matching pair of gait features are often drawn from different views due to differences in camera position/attitude and walking directions in the real world, it is important to cope with cross-view gait recognition. In this paper, we propose a discriminative approach...
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 chronogait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches...
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