An Efficient Biometric Multimodal Face, Iris and Finger Fake Detection using an Adaptive Neuro Fuzzy Inference System (ANFIS)
Samuel Wilson, A. Lenin Fred · 2014
The face, iris and finger print are among the most promising biometric authentication that can precisely identify and analysis a person as their unique textures can be quickly extracted during the recognition process. This biometric detection and authentication often deals with non-ideal scenarios such as blurred images, off-angles, reflections, expression changes. These precincts imposed by uni modal biometrics can be pound by incorporating multimodal biometrics. for this reason, in this paper, we present a new Effective fake detection method that can be used in multiple biometric systems to detect different types of fake access attempts. An important feature and objective of the proposed system is to enhance the image quality and very low degree of complexity for security of biometric recognition frameworks. For the preprocessing we used score level approach Median filter with canny edge detection and Hough transform with Anisotropic Gaussian Filter. For the Feature Extraction we have used Gabor filter. The classification is done by ANFIS which is an efficient classification. The performance of the proposed approach is validated and is efficient.