28 May 2013 Gait recognition using spatio-temporal silhouette-based features
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This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.
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Azhin Sabir, Azhin Sabir, Naseer Al-jawad, Naseer Al-jawad, Sabah Jassim, Sabah Jassim, "Gait recognition using spatio-temporal silhouette-based features", Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 87550R (28 May 2013); doi: 10.1117/12.2017950; https://doi.org/10.1117/12.2017950


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