The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $0.7415$ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %

Palmprint Recognition Using Gabor-Based Scale Orientation

Various methods are used to obtain a superior palmprint recognition system. After selecting the palmprint image filter, using the Gabor orientation scale pair becomes an option to support the improvement of the verification process. The $[8\times 7]$ pair of the Gabor orientation scale pair provides a significant system improvement impact from several alternatives. Although many researchers in the same field use different options by getting as many as 40 different positions, with differences as many as 56 parts, Gabor does not take up computational time. The system will be more superior when it combines the use of ThreeW filter, KPCA dimension reduction, and cosine matching method to get a verification rate of $99,611\%$. With the achievement of the results of this study, it can be an alternative system in the field of palmprint recognition

Palmprint Recognition Using Gabor-Based Scale Orientation

Various methods are used to obtain a superior palmprint recognition system. After selecting the palmprint image filter, using the Gabor orientation scale pair becomes an option to support the improvement of the verification process. The $[8\times 7]$ pair of the Gabor orientation scale pair provides a significant system improvement impact from several alternatives. Although many researchers in the same field use different options by getting as many as 40 different positions, with differences as many as 56 parts, Gabor does not take up computational time. The system will be more superior when it combines the use of ThreeW filter, KPCA dimension reduction, and cosine matching method to get a verification rate of $99,611\%$. With the achievement of the results of this study, it can be an alternative system in the field of palmprint recognition