Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features

Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for fingerprint. Hence, fusion of more than one biometric samples, traits or algorithms to achieve quality performance is another way to reap the better overall performance. In current scenario many researchers concentrating on Multimodal Biometrics with new fusion techniques ideas. We propose a new method of feature level fusion which uses Modified Adaptive Bilinear Interpolation (MABI) method to increase the resolution of data sample, which gives better features for fusion which gives more accurate results. In this work, experiment is done on AT&T face Cambridge University Computer Laboratory and MCYT signature Biometric Recognition Group datasets with combination of both unimodal and multimodal traits. K Nearest Neighbor (KNN) and Ensemble methods are used for classification. The proposed biometric system can be used in biometric surveillance, biometric screening for secured places, forensic applications etc.