Design of Non-contact Hand Shape Verification Method
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A new method for personal identification based on geometric invariant moment is presented. Firstly image processing including binary processing and segment of hand silhouette are used, and then translation and scale normalization algorithms are implemented on the palms and fingers image. After that the geometric moment characteristics of image are extract, and then the feature vectors composed of seven moment invariants is obtained. At last, support vector is achieved by training 100 images data in images database, 15 testing image were selected randomly to verify validity and feasibility of algorithms. Experimental results indicate that the accuracy of hand shape identification is 93%. The new method of extracting hand shape geometric moment as characteristic matrix is easy to realize with characteristic of high utility and accuracy, and solve the problem of translation, rotation and scaling during the image acquisition process without positioning aids, and especially for development and application of the portable embedded devices.
Hand shape verification, Image preprocessing, Moment invariants, Feature extraction, SVM