Face and Human Gait Recognition Using Image-to-Class Distance

• Published in: IEEE transactions on circuits and systems for video technology Vol. 20; no. 3; pp. 431 - 438
• Main Authors: Huang, Yi, Xu, Dong, Cham, Tat-Jen
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Bipartite graph; Computational efficiency; Exact sciences and technology; Face recognition; Gait recognition; Galleries; Human; human gait recognition; Humans; Image databases; Image processing; Image recognition; image-to-class distance; Information, signal and communications theory; Integer programming; Linear programming; Pattern recognition; Pixel; Preprocessing; Probes; Signal processing; Spatial databases; Studies; Telecommunications and information theory; Biometrics; Performance evaluation; Face recognition; Image processing; Spacing; Identification; Pattern recognition; image-to-class distance; Algorithm; Gait analysis; Integer programming; human gait recognition; Database; Distance measurement; Bipartite graph; Automatic recognition; Graph matching
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2009.2035852

We propose a new distance measure for face recognition and human gait recognition. Each probe image (a face image or an average human silhouette image) is represented as a set of local features uniformly sampled over a grid with fixed spacing, and each gallery image is represented as a set of local features sampled at each pixel. We formulate an integer programming problem to compute the distance (referred to as the image-to-class distance) from one probe image to all the gallery images belonging to a certain class, in which any feature of the probe image can be matched to only one feature from one of the gallery images. Considering computational efficiency as well as the fact that face images or average human silhouette images are roughly aligned in the preprocessing step, we also enforce a spatial neighborhood constraint by only allowing neighboring features that are within a given spatial distance to be considered for feature matching. The integer programming problem is further treated as a classical minimum-weight bipartite graph matching problem, which can be efficiently solved with the Kuhn-Munkres algorithm. We perform comprehensive experiments on three benchmark face databases: 1) the CMU PIE database; 2) the FERET database; and 3) the FRGC database, as well as the USF Human ID gait database. The experiments clearly demonstrate the effectiveness of our image-to-class distance.