Fractional coefficients of transformed edge palm images with Cosine, Kekre and Slant wavelet transforms for palm print identification

Paper presents extended performance comparison of palm print identification techniques based on fractional coefficients of transformed palm print edge image using Cosine, Kekre, Slant transform and respective wavelet transforms. The uniqueness of palm print is in the pattern of ridges and furrows it has. This pattern is considered here in form of the palm edge images obtained using various edge detection methods. 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. Image transforms and their wavelet transforms applied on palm print edge image and 7 ways of taking fractional coefficients along with five different edge detection methods give total 210 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 wavelet have shown performance improvement in palm print identification using fractional coefficients of transformed images considering both left and right palm print images. In all edge detection methods, Canny and Laplace have proven to be better.