Abstract
Recently, multimodal biometric systems have been widely accepted, which has shown increased accuracy and population coverage, while reducing vulnerability to spoofing. The main feature to multimodal biometrics is the amalgamation of different biometric modality data at the feature extraction, matching score, or decision levels. Recently, a lot of works are presented in the literature for multi-modal biometric recognition. In this paper, we have presented comparative analysis of four different feature extraction approaches, such as LBP, LGXP, EMD and PCA. The main steps involved in such four approaches are: 1) Feature extraction from face image, 2) Feature extraction from iris image and 3) Fusion of face and iris features. The performance of the feature extraction methods in multi-modal recognition is analyzed using FMR and FNMR to study the recognition behavior of these approaches. Then, an extensive analysis is carried out to find the effectiveness of different approaches using two different databases. The experimental results show the equal error rate of different feature extraction approaches in multi-modal biometric recognition. From the ROC curve plotted, the performance of the LBP and LGXP method is better compared to PCA-based technique.
Highlights
Over the past decade, biometric authentication has drawn substantial attention with growing demands in automated personal identification [4]
Calculation of Weightage Based on Irrelevant Pixels: The concatenated feature vector is computed for a test sample having both the iris and face images, and it is compared with the concatenated vectors of an iris image and face image of the database
From the ROC curve graph, we have analyzed that the local Gabor XOR pattern (LGXP) and Local Binary Pattern (LBP)-based method has a lower False Non-Match Rate (FNMR) value which means the better security of the proposed multimodal biometric system
Summary
Biometric authentication has drawn substantial attention with growing demands in automated personal identification [4]. Multimodal biometric systems are capable of defeating some of the restrictions of unimodal biometric systems by adding multiple sources of information for the process of personal recognition [7]. Iris is an externally visible, yet protected organ whose unique epigenetic pattern stays constant throughout the adult life [15] These features make it suitable for use as a biometric for identifying the persons. In our previous work [1], we have made use of LGXP feature as for face and iris-based multi-modal biometric recognition system. We have done a detailed analysis over our previous technique [1] with the technique given by Zhifang Wang et al [2] who combined iris and face features for multi modal biometric recognition system using PCA and Gabor feature.
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