Indian Journal of Science and Technology

Abstract

Objectives: To combine several biometric methods used for face and iris simultaneous recognition of an individual in order to enhance the performance. Methods/Analysis: For every methodology, four approaches are used for features extraction: discrete wavelet transform, singular value decomposition, discrete cosine transform and principal component analysis. Then, matching is employed by different distance measurements: City block, Euclidean, Seuclidien, Cosine, Chebychevand Correlation. Findings: The most widely used normalization method such as min-max and a new method using geometric mean is presented. The data fusion is performed at the score level with two methods: simple sum and weighted sum. The obtained comparison results show that PCA (face) and PCA (iris) fusion scenario associated with simple sum rule and the proposed new normalization method (geometric mean) have given the best recognition rate. Application/Improvements: This new normalization method helps considerably to enhance other methods in a multimodal biometric recognition system.

Keywords: Distances, Face, Fusion, Geometric Mean, Iris, Multimodal Biometrics, Recognition Rate