Abstract

Unimodal biometric systems when compared with multimodal systems can be easily spoofed and may get affected by noisy data. Due to the limitations faced by unimodal systems, the need for multimodal biometric systems has rapidly increased. Multimodal systems are more reliable as it uses more than one independent biometric trait to recognize a person. These systems are more secured and have less enrollment problems compared to unimodal systems. A new Enhanced Local Line Binary Pattern (ELLBP) method is devised to extract features from ear and fingerprint so as to improve recognition rate and to provide a more reliable and secured multimodal system. The features extracted are stored in the database and compared with the test features for matching. Hamming distance is used as the metric for identification. Experiments were conducted with publicly available databases and were observed that this enhanced method provides excellent results compared to earlier methods. The method was analyzed for performance with Local Binary Pattern (LBP), Local Line Binary Pattern (LLBP) and Local Ternary Pattern (LTP). The results of our multimodal system were compared with individual biometric traits and also with ear and fingerprint fused together using enhanced LLPD and other earlier methods. It is observed that our method outperforms earlier methods.

Keywords:

Ear , fingerprint , LBP, LLBP , LTP, multimodal biometrics, unimodal biometrics,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.