Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Individual recognition from periodic activity using hidden Markov models
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
On automated model-based extraction and analysis of gait
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Gait recognition using independent component analysis
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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Gait-based human identification is a challenging problem and has gained significant attention. In this paper, a new gait recognition algorithm using Hidden Markov Model (HMM) is proposed. The input binary silhouette images are preprocessed by morphological operations to fill the holes and remove noise regions. The width vector of the outer contour is used as the image feature. A set of initial exemplars is constructed from the feature vectors of a gait cycle. The similarity between the feature vector and the exemplar is measured by the inner product distance. A HMM is trained iteratively using Viterbi algorithm and Baum-Welch algorithm and then used for recognition. The proposed method reduces image feature from the two-dimensional space to a one-dimensional vector in order to best fit the characteristics of one-dimensional HMM. The statistical nature of the HMM makes it robust to gait representation and recognition. The performance of the proposed HMM-based method is evaluated using the CMU MoBo database.