Histogram of Oriented Gradients Feature Extraction in Enhanced Convolutional Neural Networks Method for Palm Vein Recognition

Histogram of Oriented Gradients, or HOG, is an efficient method for extracting features from images that shows to be reliable in recognizing and detecting objects in a variety of conditions. HOG is a very useful tool in image analysis because it is invariant to brightness, rotation, and scale transformations and can generate detailed local descriptors. By exploiting the advantages of HOG in feature extraction, the palm vein recognition system can overcome the weaknesses of palm vein images, which are sensitive to changes in brightness and rotation. By analyzing the distribution of orientation gradients, HOG can extract important features from palm vein images generated from infrared light, enabling reliable biometric recognition. We proposed a method applying HOG into palm vein biometric system with Convolutional Neural Network (CNN) which has strong hierarchical representation capabilities, automatic feature extraction, and the ability to handle high-dimensional data. Our proposed model is tested on three public contactless palm vein datasets (CASIA and VERA). We evaluated the results using various performance biometric measures, such as correct recognition rate (CRR) accuracy, and area under curve (AUC) of the receiver operating characteristic. The simulation results showed that HOG features could improve accuracy from pretrained CNN such as AlexNet, VGG-16, ResNet-50 and EfficientNet.