DCT feature based fingerprint recognition

In the fingerprint recognition application utilizing more information other than minutiae is much helpful. In this paper we proposed discrete cosine transform (DCT) based feature vector for fingerprint representation and matching. The transform is applied on the image directly without any preprocessing. By dividing the transformed image into various blocks, standard deviation is calculated for each block and such 96 standard deviations will form the feature vector. This feature vector is used in matching stage. Work is further extended by forming feature vector of 36 standard deviation from mid and high frequency bands of DCT coefficients. Total 8 images per person are captured for 15 individual and training set is prepared with the help of k images where k varies from 1 to 8. Results are checked against remaining images as well as newly captured image of same person. Results are represented in terms of Truly accepted, Falsely accepted and Rejected users. Recognition rate (%) are calculated with different threshold values. In identification mode 100% results are obtained for a typical threshold value.