Abstract
ABSTRACT 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.
Highlights
The identifiers related to biometric are distinguishing and quantifiable characteristics used to label and trace individual human beings
This, in turn, reduces the computational time by 11% compared to Low-Rank and Joint Sparse Representations (LR-JSR) and 25% compared to Discriminant Correlation Analysis (DCA)
Based on the Log Likelihood Ratio, matching between the training and test set was conducted based on the mutual information values, that in turn improved the recognition rate by 44% compared to LR-JSR and 24% compared to DCA
Summary
The identifiers related to biometric are distinguishing and quantifiable characteristics used to label and trace individual human beings. Certain wellestablished biometrics used for human identification is face, fingerprint, palm, ear, voice and so on. Many of the real-world biometric systems referred to as the unimodal; heavily depend on the single source of biometric information. Multimodal biometric system observes two or more features from human biometric sample to determine a person’s authentication. Multimodal biometric system highly increases the recognition performance. Because, combining multiple pieces of evidence (i.e. multimodal features) for human identification is more effective and reliable. The problem of information fusion still needs to be improved for optimizing the recognition rate of multimodal biometric system
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