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.
Highlights
Single biometric trait is used in unimodal systems to identify a person
A new texture descriptor called Local Line Binary Pattern (LLBP) was proposed and implemented by Rosdi et al (2011) to extract features from finger vein images. By this enhancement it was shown that the Equal Error Rate (EER) for the LLBP is less when compared with Local Binary Pattern (LBP) and LDP and the time required to extract the features using LLBP was proven to be less
Feature extraction from Region of Interest (ROI) of fingerprint and ear image: Feature extraction by ELLBP: The required features from ear and fingerprint are extracted by applying the new method Enhanced Local Line Binary Pattern (ELLBP): In Local Line Binary Pattern (LLBP) binary codes are generated from the image
Summary
Single biometric trait is used in unimodal systems to identify a person. Few issues like spoofing, non universality, intra class variations etc., affects unimodal system. To overcome the problems of unimodal systems multimodal systems were introduced which uses more than one biometric trait to identify a person. Match score level fusion: The match score output from multiple biometric matchers are combined to generate a new match score. Matching the extracted features can be done by Hamming distance or Euclidean distance: Chung (2007) for face recognition. Another variant of LBP was presented by Nanni et al (2010) for medical image analysis where quinary encoding in elliptical neighborhood was used for the analysis. Local Ternary Pattern (LTP) is an extension of Local Binary Pattern (LBP) This is developed as powerful descriptor for noised images. We have proposed and implemented an Enhanced LLBP to extract the features from ear image and fingerprint image
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