Abstract-Biometric user authentication techniques for security and access control have evoked an enormous interest by science, industry and society in the last two decades. But even the best single biometric system suffers from spoof attacks, intra-class variability, noise, susceptibility etc. In the realm of biometrics, the consolidation of evidence presented by multiple biometric sources is an effective way of enhancing the recognition accuracy of an authentication system. This paper proposes an authentication for a multimodal biometric system identification using two traits i.e., face and palmprint at feature extraction level. The training database consists of face and palmprint images. Principal Component Analysis method is used to extract the features from face and palmprints separately. The feature normalization and feature concatenation scheme followed by a dimensionality reduction procedure is adopted to form the feature matrix. The normalized match (distance) scores generated by respective palm and face features before fusion are used to form fused match score. The Euclidean distance and the feature distance are calculated after fusion. All three distances are used to arrive at final decision. Feedback routine implemented between the feature extraction and the matching modules of the biometric system can lead to substantial improvement in multimodal matching performance.

One of the basic requirements of our modern day society is personal authentication. Biometric recognition should make a human‐like identity determination by identifying its physiological and/or behavioral characteristics. In comparison to traditional knowledge‐based approaches, biometric identification systems have the potential to bring benefits. However, because of the difficulties in extracting non‐class discriminative features, the lack of protection during storage of extracted features, and poor recognition accuracy, most frequently used biometric systems lack model protection and robustness. This research proposed a Mimus multimodal biometric system focused on the combination of multiple modalities and optimal level fusion of features to resolve these problems. Initially, the novel Blob‐funk method extracts the complementary non‐class discriminatory information among different modalities, which accomplishes the biometric data enrollment. Thus, it extracts the different properties by comparing surrounding regions based on finding the local maxima and minima of the function. After extracting the features, they need to be stored in a secure manner in a database. Therefore, the paper incorporates the new code block protection strategy to achieve an effectual protection of continuous monitoring via the generation of non‐invertible features, which is used to create the templates, thus storing them in a database. Finally, the novel Lucynomial logistic regression system incorporates user authentication and thus achieves greater recognition accuracy through estimation of threshold value with confrontation of spoof attacks. Hence, compared to the existing techniques such as SVM, PCA, and DBN, the outcome of the proposed work attains 97.53% accuracy, 0.020% FAR, 96.44% recall, and 97.85% precision, thus exemplifying the competence of the novel system.

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