In order to improve the classification rate of gait recognition, a new gait recognition algorithm is proposed. Firstly, the gait images are preprocessed, and the outlines of gait images are extracted and normalized. Secondly, wavelet moments of the outlines are calculated to describe the static feature of the gait images. Thirdly, the leg double triangle model is built. The first triangle consists of the mid-point of the two hips, left knee point and right knee point, and the other one consists of the mid-point of the two hips, left ankle point and right ankle point. Then the parameters of two triangles are extracted to describe the dynamic features of the gait images. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.
In order to improve the performance of muti-digital rights protection, a multiple watermarking algorithm based on Discrete Cosine Transform (DCT) and spread-spectrum technique is proposed. First, the host image is divided into subblock and each block is manipulated by DCT. Second, two watermarks are encrypted by Arnold scrambling. Third, the watermarks are modulated by by utilizing two kinds of strategies, and then are embedded in middle frequency coefficients of DCT domain. Experimental results show that the proposed algorithm is efficient to resist traditional signal processing attacks and has better security and robustness, higher capacity.
In order to enhance the accuracy of gait recognition, a new gait feature extraction algorithm is proposed. Firstly, the gait images are preprocessed to extract moving objects, including background modeling, moving object extracting and morphological processing. Secondly, an equidistant slicing curve model based on system of polar coordinate is designed to slice the moving object, and the slicing vector is used to describe the spatial feature; Thirdly, the slicing vector is converted into frequency signal by Fourier transform to extract the frequency feature. Finally, the above two features are fused and used for the classification. The experimental results show that proposed algorithm provides higher correct classification rate than the algorithms using single feature, and meets the requirements of the real-time.
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