Multimodal Biometrics Based on Fingerprint and Finger Vein

Biometric systems identify a person through physical traits or verify his/her identity through automatic processes. Various systems were used over years including systems like fingerprint, iris, facial images, hand geometry and speaker recognition. For biometric systems successful implementation, it has to address issues like efficiency, accuracy, applicability, robustness and universality. Single modality based recognition verifications are not robust while combining information from different biometric modalities ensures better performance. Multimodal biometric systems use multiple biometrics and integrate information for identification. It compensates unimodal biometric systems limitations. This study considers multimodal biometrics based on fingerprint and finger veins. Gabor features are extracted from finger vein using Gabor filter with orientation of 0, 15, 45, 60 and 75°, respectively. For fingerprint images, energy coefficients are attained using wavelet packet tree. Both features are normalized using min max normalization and fused with concatenation. Feature selection is through PCA and kernel PCA. Classification is achieved through KNN, Naïve Bayes and RBF Neural Network Classifiers.