Informatics Publishing Limited

S. Ashath Thauth ¹, K. Krishneswari ²

Affiliations
1 Department of Computer Science and Engineering, Tamilnadu College of Engineering, Coimbatore, Tamilnadu, IN
2 Department of Computer Science and Engineering, Anna University College of Technology, Coimbatore, Tamilnadu, IN

Abstract

Biometrics is the technology used for measuring and analyzing a person's unique characteristics. To overcome the problems in unimodal biometric authentication we present a multimodal approach based on feature level biometric fusion. We combine two kinds of biometrics:one is the palmprint feature and another one is fingerprint. The palm area contains a large number of features such as principal lines, wrinkles, delta points, minutia, and texture features. Fingerprints and palmprints have a unique structure formed by ridges, valleys and sweat pores present on the skin. Fingerprints have special features caused by ridge endings, bifurcations and loops. In this paper we propose a feature level fusion technique that extracts the discriminant features using gabor based image preprocessing and principal component analysis (PCA) from both palmprint and fingerprint. And then a statistical based normalized weighting strategy is used to conduct feature level fusion. Using PolyU palmprint and fingerprint database as the test data, the experimental results show that the presented approach significantly improves the recognition effect of single sample biometrics problem.

Keywords

Multimodal Biometrics, Feature Level Fusion, Gabor Filter, Principal Component Analysis (PCA).

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K. Krishneswari ¹, S. Arumugam ²

Affiliations
1 Department of Computer Science and Engineering, Tamilnadu College of Engineering, IN
2 Nandha Educational Institutions, IN

Abstract

Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In feature extraction, the single feature has become bottleneck in producing high performance. To solve this we propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiple features like Texture (Gabor), and Line features from the preprocessed palmprint images. The feature vectors obtained from different approaches are incompatible and also the features from same image may be redundant. Therefore, we propose a Particle Swarm Optimization (PSO) based technique to perform feature fusion on extracted features. Being an iterative technique that randomly optimizes the fused feature space, it overcomes the problems of feature fusion. Finally the feature vector is further reduced using Principal Component Analysis (PCA) and matched with stored template using NN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 200 users. The experimental results illustrates that the feature level fusion improves the recognition accuracy significantly.

Keywords

Biometrics, Palmprint, Feature Fusion, PSO, Intramodal.

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