The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Curve spreads: a biometric from front-view gait video
Pattern Recognition Letters
Study of Different Fusion Techniques for Multimodal Biometric Authentication
WIMOB '08 Proceedings of the 2008 IEEE International Conference on Wireless & Mobile Computing, Networking & Communication
Frontal-view gait recognition by intra- and inter-frame rectangle size distribution
Pattern Recognition Letters
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Gait energy volumes and frontal gait recognition using depth images
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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Automatic human recognition systems based on biometrics are becoming increasingly popular in today's world. In particular, gait recognition (i.e., recognition based on the peculiar manner of walking) is especially interesting since it allows the recognition even at a distance, without explicit user co-operation. In this paper, depth information is integrated in a silhouette-based gait recognition scheme, in order to produce a hybrid 2D-3D frontal gait recognition scheme. The depth information of the human silhouette is obtained with a Kinect camera, from which a three dimentional (3D) human point cloud is obtained. In the proposed multimodal framework, feature extraction is done by considering the full 3D human point cloud, as well as three 2D projections of it, corresponding to the top, front and side-views of the human subject. The experimental results for this multimodal scheme, based on both 3D and 2D data, show superior recognition rates, when compared to existing front-view gait recognition schemes.