Feature Recognition Of Palm Vein Based On Neural Network

The data is hailed as 'the oil and diamond mine of the 21 st century' and the security of data is the heart of cyberspace security.Biometrics is the first checkpoint to effectively protect data security.The human palm vein information has the living characteristics that other biological characteristics do not have,so it has received great attention from governments and research institutions.Based on the neural network's machine learning and deep learning techniques have achieved great success in the field of pattern recognition,and provide a way to research how to improve the performance of existing palm vein feature recognition technology.In order to use the neural network to finish palm vein recognition on a general-purpose office computer and accelerate the popularization and promotion of palm vein recognition technology,this paper studied the feature extraction and recognition methods of the palm vein which based on the neural network structure and used the palmar NIR image library of Hong Kong Polytechnic University as experimental data,with the help of neural network and machine learning technology.In order to ensure that more palm vein detail information can be extracted and the recognition accuracy can be improved,this paper proposes a kind of algorithm combined with human brain visual information process model which called HMAX model with the help of texture enhancement algorithm and pixel layer fusion algorithm.A new palm vein feature extraction algorithm's structure is a hierarchical structure similar to neural network.After a series of experimental tests,it is concluded that the feature extraction algorithm is more than three percentage points higher than the recognition accuracy of the palm vein features extracted by LBP and PCA and can reach 96.83%.Since the convolutional neural network can extract more essential palm vein features and at the same time save some of the pre-processing process,in order to further study the accuracy of palm vein feature recognition under convolutional neural network structure,this paper designed a CNN structure for finishing the palm vein feature recognition.The improved convolutional neural network is based on LeNet-5 and AlexNet's structure.After a series of experimental tests,the recognition rate of the optimized network model reached 98.5%,which is 1.3 percentage points higher than the recognition rate of the palm vein feature extraction algorithm proposed before this paper.It proves that the algorithm can be used in general performance computers,and the convolutional neural network is effectively used on the computer to identify and classify the characteristics of the palm vein,which has the potential to be further promoted and applied.