Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Multilinear Tensor-Based Non-parametric Dimension Reduction for Gait Recognition
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multimedia Tools and Applications
Low-resolution gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Chrono-gait image: a novel temporal template for gait recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Gait flow image: A silhouette-based gait representation for human identification
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AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Pattern Recognition Letters
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Image and Vision Computing
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We present a robust representation for gait recognition that is compact, easy to construct, and affords efficient matching. Instead of a time series based representation comprising of a sequence of raw silhouette frames or of features extracted therein, as has been the practice, we simply align and average the silhouettes over one gait cycle. We then base recognition on the Euclidean distance between these averaged silhouette representations. We show, using the recently formulated gait challenge problem (www.gaitchallenge.org), that the improvement in execution time is 30 times while possessing recognition power that is comparable to the gait baseline algorithm, which is becoming the comparison standard in gait recognition. Experiments with portions of the average silhouette representation show that recognition power is not entirely derived from upper body shape, rather the dynamics of the legs also contribute equally to recognition. However, this study does raise intriguing doubts about the need for accurate shape and dynamics representations for gait recognition.