Description

Here we provide the novel method in recognition hand and voice biometrics. In preprocessing we remove the noise from the image. For removing noise here we use the imfilter function. Then we segment hand from the image, after that we segment the finger from the segmented image. From that we calculate the length of the finger. Then we extract the feature for voice. We use GMM and MFCC for voice feature extraction. Hand biometrics applied to images acquired from a mobile device. The system offers the possibility of identifying individuals based on features extracted from hand pictures obtained with a low-quality camera embedded on a mobile device. Furthermore, the acquisitions have been carried out regardless illumination control, orientation, distance to camera, and similar aspects. Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM.