SOFT BIOMETRIC TRAIT ON FINGERVEIN RECOGNITION USING CNNRESNET

Many finger vein feature extraction algorithms achieve adequate performance due to their ability to reflect texture, while simultaneously ignoring the finger tissue-forming intensity distribution and in some cases processing it as background noise. Use this kind of noise as a novel soft biometric feature in this project to achieve better output in finger vein recognition. First, a detailed analysis of the finger vein imaging theory and the image characteristics is provided to demonstrate that the intensity distribution produced in the background by the finger tissue can be extracted for identification as a soft biometric feature. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. In the classification stage developed a system with implementation of convolution neural network specifically resnet18 for the training image dataset and image retrieving process is done. Purpose of introducing deep learning in developing finger vein identification system is to get accurate more performance and speedy results. Results are computed on the basis Euclidean distance between features obtained from test image and features of trained images, the model designed has good robustness in illumination and rotation.