Personal Authentication Using Palmprint with Phase Congruency Feature Extraction Method

Palmprint recognition is a biometric method to automatically identify a person’s identity. In this paper, phase congruency method is proposed to extract features from a palm-print image for authentication. The phase congruency is one of the promising methods to analyse the image as it is invariant to image contrast and therefore can extract reliable features under varying illumination conditions. In this paper, the phase congruency features in 6 different orientations are arranged in such a manner to get the best combination of orientations for authentication. The hand image is pre-processed to get the desired Region of Interest (ROI) / palmprint. The palmprint features are extracted by phase congruency method and are stored in feature vector. The 20 different types of feature vectors are prepared using different combination of orientations. Hamming distance similarity measurement with Sliding window is used to compare the similarity/dissimilarity between two feature vectors. Experiments were developed on a database of 600 images from 100 individuals, with five image samples per individual for training and one image sample per individual for testing. The accuracy of 97.3% can be achieved using FV11.