Biometric Authentication Using CNN Features of Dorsal Vein Pattern Extracted from NIR Image

Biometric authentication is a process of identifying and differentiating individuals for security purpose that counts on the distinctive physiological and behavioral characteristics of a person for verification. Dorsal vein pattern is one of the prospective biometrics and we have made successful use of this biometric feature for authentication purpose. This paper describes a novel design and its implementation for identifying individuals on the basis of their vein pattern of dorsal hand. The prime goal has been to establish a method with better Correct Recognition Rate (CRR), low False Acceptance Rate (FAR) and low False Rejection Rate (FRR). For our paper we have used Near Infrared (NIR) image of dorsal hand as it provides better resolution of vein pattern in the image than visible light. This recognition system consists of several steps; they are denoising of the input image using Laplacian Scale Mixture Modeling (LSM), Region of Interest (ROI) extraction from denoised image using valley point detection method, contrast enhancement using pyramid based edge aware filtering method, contrast limited adaptive histogram equalization, binarization, several serial morphological operations and finally authentication using neural networks and Support Vector Machine (SVM) classifier. The experiment was initially performed on a database of 16 distinct subjects where there are 10 raw images of each subject taken in different conditions. The accuracy obtained from experimental results is 96.63%. The scale of the experiment can be extended for more users, which we believe will yield similar success and thus this method of biometric authentication using dorsal vein pattern can be used in security purpose.

[1]  M. Omair Ahmad,et al.  Mixed Gaussian-impulse noise reduction from images using convolutional neural network , 2018, Signal Process. Image Commun..

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  M. Faundez-Zanuy,et al.  Thermal hand image segmentation for biometric recognition , 2013, IEEE Aerospace and Electronic Systems Magazine.

[4]  Chuck Wilson.  Vein Pattern Recognition: A Privacy-Enhancing Biometric , 2010 .

[5]  Jan Kautz,et al.  Local Laplacian filters: edge-aware image processing with a Laplacian pyramid , 2011, SIGGRAPH 2011.

[6]  Arun Ross,et al.  Fingerprint matching using minutiae and texture features , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Yanuar Adhinagara,et al.  Implementation of multimodal biometrics recognition system combined palm print and palm geometry features , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[8]  Septimiu Crisan,et al.  Radiation optimization and image processing algorithms in the identification of hand vein patterns , 2010, Comput. Stand. Interfaces.

[9]  Guangming Shi,et al.  Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation , 2017, IEEE Transactions on Image Processing.

[10]  Lingyu Wang,et al.  A Thermal Hand Vein Pattern Verification System , 2005, ICAPR.

[11]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[12]  M. Grgic,et al.  A survey of biometric recognition methods , 2004, Proceedings. Elmar-2004. 46th International Symposium on Electronics in Marine.