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Highly Efficient Approaches for Biometric Security Systems

Journal: International Journal of Linguistics and Computational Applications (Vol.4, No. 1)

Publication Date:

Authors : ;

Page : 1-6

Keywords : Biometric; Artificial Neural Network; Probabilistic Neural Network Classifier; Gray-Level Concurrence Matrix; Non-Subsampled Contourlet Transform;

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— This paper proposes to implement the biometric security system spoofing detection on palm vein, face and iris image patterns. A Biometric system is essentially a pattern recognition system that makes use of bio-metric traits to recognize individuals. In a biometric security system, the data hiding approach is involved to conceal the secret personal informatics for enhancing the privacy protection. There are negative effects on recognition performance on fingerprint and palm print biometrics due to the some factors like the dryness or dirt in the finger and it also varies with age, for instance, this system is not appropriate for children, because the size of their fingerprint changes quickly. This paper depicts a proposed method which avoids the above negative factors. Palm vein, face and Iris patterns stand out from the host of intrinsic biometric traits for the development of a recognition system that can meet all the security expectations of a biometric system. In the proposed method palm vein, face and iris image patterns spoofing can be easily detected using Neural Network (NN) with the help of GLCM properties. Vein patterns are the network structure of blood vessels underneath the human skin that are almost invisible to the naked eye under natural lighting conditions and can be acquired only when employing infrared illumination. The texture of the blood vessels and Iris of different individuals has been proven to be distinctive even among identical twins. The selected image of palm veins, face and Iris is aligned and cropped according to the key points. The image is enhanced and resized. The features of palm vein, iris and face are compared with database image feature vectors and are recognized using Probabilistic Neural Network classifier (PNN). Finally the performance of multimodal system along with stenographic approach will be measured with accuracy and it proves to provide better matching rate than earlier approaches

Last modified: 2017-12-23 04:25:56