Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition system can be extremely complex due to high dimension unimodal features in temporal domain. This paper explains a deep multimodal biometric system for human recognition using three traits, face, fingerprint and iris. With the objective of reducing the feature vector dimension in the temporal domain, first pre-processing is performed using Contourlet Transform Model. Next, Local Derivative Ternary Pattern model is applied to the pre-processed features where the feature discrimination power is improved by obtaining the coefficients that has maximum variation across pre-processed multimodality features, therefore improving recognition accuracy. Weighted Rank Level Fusion is applied to the extracted multimodal features, that efficiently combine the biometric matching scores from several modalities (i.e. face, fingerprint and iris). Finally, a deep learning framework is presented for improving the recognition rate of the multimodal biometric system in temporal domain. The results of the proposed multimodal biometric recognition framework were compared with other multimodal methods. Out of these comparisons, the multimodal face, fingerprint and iris fusion offers significant improvements in the recognition rate of the suggested multimodal biometric system

Review of Multimodal Biometric Identification Using Hand Feature and Face

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits