Multimodal biometric authentication algorithm using ear and finger knuckle images

Biometrics that use physiological traits such as face, iris, fingerprints, ear, and finger knuckle (FK) for authentication face the problems of noisy sensors data, non-universality, and unacceptable error rates. Multimodal biometric methods use different fusion techniques to avoid such problems. Fusion methods have been proposed in different levels such as feature and classification level. This paper proposes two multimodal biometric authentication methods using ear and FK images. We propose a method based on fusion of images of ear and FK before the feature level, thus there is no information lost. We also propose a multi-level fusion method at image and classification levels. The features are extracted from the fused images using different classifiers and then combine the outputs of the classifiers in the abstract, rank, and score levels of fusion. Experimental results show that the proposed authentication methods increase the recognition rate compared to the state-of-the-art methods.