Finger Vein Recognition Based on PCA Feature using Artificial Neural Network

Personal recognition technology is developing rapidly as a security system. Traditional methods such as authentication key; password: card is not secure enough, because they could be stolen or easily forget. Biometrics has been applied to a wide range of systems. According to various researchers, vein biometrics was a good technique from other biometric authentication system used, such as fingerprints, hand geometry, voice, etc. of the DNA. Root Authentication systems can be designed in different ways. All methods include the matching stage. A neural network is an effective way of matching Personal identification authentication system. The finger vein pattern is unique biometric identity of the human beings. The finger vein recognition is a popular biometric technique which is used for authentication purposes in various applications. In the propose work an algorithm is proposed to find the accuracy, FRR and FAR of finger vein recognition. The performances of PCA, threshold segmentation, pre-processing and testing & training techniques has been validate and compared with each other in order to determine the most accurate results in terms of finger vein recognition

Finger vein verification

At present, biometric system is well - liked as its high security level manage to reduce frauds, intrudes and forgeries. A biometric system utilizes physiological features and behavior characteristics of an individual such as face, finger mark, iris, handwriting, voice, signature and others. One of the recent biology feature used as biometric system is the finger vein. The vein features are robust, stable and most importantly unique for every individual. This trait offers a higher security because for gery is extremely difficult. The finger vein verification project verifies a person‟s identity based on the vein patterns. Generally, the finger vein images are pre - processed and a neural network algorithm is developed to verify the finger vein images. Las t but not least, the performance of the finger vein verification is evaluated. The project achieved an overall accuracy of 82.86%

Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification

Recently, finger vein biometric identification methods have had more attention among the researchers due to its various advantages such as: uniqueness to individuals, immunity to ages and invisibility to human eye (hard to duplicate). Many improvements methods were utilized to increase the speed and accuracy of the identification. Feature extraction techniques based on global feature extraction such as Principle Component Analysis (PCA) were implemented. However, the results did not show robustness to occlusions and misalignments on the finger vein images. Therefore, local feature extraction techniques were used to overcome these issues. A pooling based feature extraction technique for finger vein identification was implemented in this research. The proposed algorithm extracted the local feature information of the finger vein pattern (patches), and used these patches to improve the robustness of the identification. The algorithm was mainly inspired by spatial pyramid pooling in generic image classification combined with PCA. With patch size = 4, four pyramid levels = [1x1, 2x2, 3x3, 4x4] and ~38 % dimension reduction on the extracted features vector (10 PCA coefficient), the accuracy of the identification was 88.69 % which was higher than PCA by 10.10%. The proposed algorithm was implemented on hardware using Verilog-HDL, and targeting Field Programmable Gate Array (FPGA) applications. The result showed an outstanding speed improvement compared to software implementation. The time consumed by the hardware for extracting the features of one image was 310X time faster than the consumed time for software implementation. With those improvements in accuracy and the speed, the proposed algorithm contributes to the advancement of finger vein biometric system

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition