Palm print identification using fractional coefficient of transformed edge palm images with Cosine, Haar and Kekre transform

Paper presents performance comparison of palm print identification techniques based on fractional coefficients of transformed palm print edge image using three transforms namely Cosine, Haar and Kekre. In transform domain, the energy of image gets concentrated towards low frequency region; this characteristic of image transforms is used here to reduce the feature vector size of palm print images by selecting these low frequency coefficients in transformed edge images. Three image transforms applied on palm print edge image and 7 ways of taking fractional coefficients along with five different edge detection methods give total 105 variation of proposed palm print identification method. Experimentation is done on a test bed of 1000 palm print images (500 left and 500 right).Genuine acceptance rate is considered for performance comparison. The experimental results in Cosine and Haar transform have shown performance improvement in palm print identification using fractional coefficients of transformed images. In all edge detection methods, canny is proven to be better. In all Cosine transform gives best performance having maximum GAR value for canny edge detection method with 0.097% of fractional coefficients considering both left and right palm print images.