Abstract

Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In feature extraction, the single feature has become bottleneck in producing high performance. To solve this we propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiple features like Texture (Gabor), and Line features from the preprocessed palmprint images. The feature vectors obtained from different approaches are incompatible and also the features from same image may be redundant. Therefore, we propose a Particle Swarm Optimization (PSO) based technique to perform feature fusion on extracted features. Being an iterative technique that randomly optimizes the fused feature space, it overcomes the problems of feature fusion. Finally the feature vector is further reduced using Principal Component Analysis (PCA) and matched with stored template using NN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 200 users. The experimental results illustrates that the feature level fusion improves the recognition accuracy significantly.