Ali Salam Aljaberi

The recognition of hand palm print through veins is one of the promising biometric techniques, which has received great interest lately due to its accuracy in identifying individuals. Although the literature witnessed several techniques and devel-opments to deal with the problem of identifying people through the veins in the palm, the technology is still in its infancy. In this research, we propose our palm print recognition model which use convolution neural networks preceded by the pre-processing stages to optimise the data and to extract the important regions. The pre-processing helped in extracting the vein pattern which feed into the proposed convolution neural network model. The CASIA database has been used; it contains 7200 images taken form 100 people based on 6 wavelengths (940 nm, 850 nm, 700 nm, 630 nm, 460 nm, and white). The model has been tested with all wavelengths in the database. AlexNet is used for benchmarking. The results show that our approach using the proposed pre-processing has helped to surpass AlexNet in terms of performance, speed, and accuracy.

This paper presents a new validation method using a convolutional neural network for palm vein recognition. Unlike fingerprint and face. Vein patterns are endogenous biometric features that do not change over time and that make them difficult to identify and replicate in people. The proposed paper aims to provide a new way to identify people through their veins. This paper used the CASIA dataset, which consists of several wavelengths, in this research used the 850nm wavelength, which is clear in the veins, In addition, we divided the data into 3 cases. The first case is when the training and testing ratio is 50/50, the second case when it is 70/30, and the last case when it is 90/10. Obtained an accuracy of 98% in the case 90/10. In addition, to the proposed network, and used a well-known global network, the AlexNet network, where did the same work on it to compare the results of our proposed network with it. As proposed network outperformed it in terms of accuracy and speed, where the accuracy was 96% in the case 90/10.

With the development of technology, recognition has become now a very important and main thing due to the large number of fraud and forgery operations that occur. The traditional methods such as signature and recognition based on knowledge do not meet the safety requirements due to the ease of forging and stealing them, as biometric technologies have emerged as an alternative to them due to the high security provided and accuracy. Biometrics rely on behavioral and physiological characteristics such as recognition based on palm veins to provide maximum protection and avoid unauthorized access to our personal property. In this paper, we will talk about authentication through palm veins. They are biological properties that do not change over time, and each person has his own pattern of veins, even symmetric twins have different patterns. These veins are not seen with the eye, which is difficult to forge and steal as the pattern of veins is extracted by near infrared rays palm veins images are considered one of the most promising technologies that meet the advanced safety requirements of modern privacy policies for highly sensitive systems. The authentication system through palm veins generally content from four steps, namely acquisition of image, preprocessing, extraction of feature and feature matching. In each stage, there is a set of methods used in this research. We will talk about these methods used in the palm vein by using the cassia database.