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In this paper, we solve the searching problem by high level features used by sign language recognition. Firstly, we find the face in video frames that has complex background, and then we find the left sign and right sign in specific areas. By computing the signs' length, position, velocity, acceleration, Fourier figure descriptor and etc, we generate the signs' dynamic features. Consequently, we segment...
Object tracking based on color feature often fails in a complex background. To deal with this problem, a particle filtering object tracking approach is proposed in this paper based on local binary pattern and color feature. Color histogram is the global description of targets in color image, while local binary pattern texture contains information of neighbor region texture in gray image. These two...
Automatic face recognition system based on local feature detection and feature extraction techniques is presented. The method works on color face images and performs face localization initially. It then detects and selects important fiducial facial points and characterizes them by bank of Gabor filters (jets). A well known PCA technique is used to reduce the dimensionality of jets and recognition...
The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and...
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