Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
The i3DPost Multi-View and 3D Human Action/Interaction Database
CVMP '09 Proceedings of the 2009 Conference for Visual Media Production
Human identification from human movements
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper a novel view-invariant human identification method is presented. A multi-camera setup is used to capture the human body from different observation angles. Binary body masks from all the cameras are concatenated to produce the so-called multi-view binary masks. These masks are rescaled and vectorized to create feature vectors in the input space. A view-invariant human body representation is obtained by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). Fuzzy vector quantization (FVQ) is performed to associate human body representation with movement representations and linear discriminant analysis (LDA) is used to map movements in a low dimensionality discriminant feature space. Two human identification schemes, a movement-specific and a movement-independent one, are evaluated. Experimental results show that the method can achieve very satisfactory identification rates. Furthermore, the use of more than one movement types increases the identification rates.