Feature Level Fusion of Palm Veins and Signature Biometrics

Traditional biometric systems that based on single biometric usually suffer from problems likeimposters' attack or hacking, unacceptable error rate and low performance. So the need of using multimodal biometric system occurred .In this paper, a study of multimodal palm veins and signature identification is presented.Features of both modalities are extracted by using morphological operations and Scale Invariant Features Transform (SIFT)algorithm and a comparison for both methods is developed.Feature level fusion for both modalitiesis achieved by using a simple sum rule. Fused features vectors are subjected to discrete cosine transform (DCT) to reduce their dimensionalities. Linear Vector Quantization (LVQ) classifier is used with changed parameters to classify the different people in the database. Preliminary results are encouraging and supporting using palm veins and signature as a robust and reliable identification system.