Title :
Gait Recognition via Fused Hidden Markov Models
Author_Institution :
Shaanxi Inst. of Educ., Xi´´an, China
Abstract :
This paper presented a novel gait recognition approach based on Haar wavelet and fused Hidden Markov Models. It solves the problem that key points in each region represent gait feature insufficiently. Firstly, images from video sequences are converted into binary silhouette. Haar wavelet transform is employed to obtain key points for distinct features, and the key points are analyzed. Two sub images are utilized to represent gait features in each silhouette, and employ Principal Component Analysis to reduce its dimensionalities. Finally, fused Hidden Markov Models are employed to train and test, and it is helpful in analyzing features. Consequently, we can not only simplify the process, but also improve the recognition accuracy.
Keywords :
Haar transforms; biometrics (access control); feature extraction; gait analysis; hidden Markov models; image recognition; image sequences; principal component analysis; wavelet transforms; Haar wavelet transform; binary silhouette; fused hidden Markov model; gait feature representation; gait recognition; principal component analysis; video sequence; Databases; Feature extraction; Hidden Markov models; Principal component analysis; Vectors; Wavelet transforms; Haar wavelet; feature extraction; fused Hidden Markov Models (FHMM); gait recognition;
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2011 Third International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4577-1795-6
DOI :
10.1109/MINES.2011.62