Abstrait

Multiple features and classifiers for vein based biometric recognition

Bharathi Subramaniam, Sudhakar Radhakrishnan

An effective fusion scheme is necessary for combining features from multiple biometric traits. This paper presents a method of fusion using multiple features from hand vein biometric traits for Multimodal biometric recognition. In the proposed method, a biometric authentication system using three different set of veins images, such as, finger vein, palm vein and dorsal vein is developed. Here the multiple features from the input vein images are extracted by applying Radon transform, Hilbert– Huang transform and Dual tree complex wavelet transform for each of the vein images. Once the features are extracted, a feature level fusion is carried out using the optimization algorithm called, Group Search Optimization. Then, recognition is done using the trained features by different classifiers such as support vector machine, fuzzy, neural network, bayes classifier and k-nearest neighbor classifiers. This approach is tested on the standard data bases of finger vein, palm vein and dorsal vein images of the hand. The proposed method provides higher accuracy and lower equal error rate which shows the efficiency of the technique compared with the other existing techniques.