Human Face Recognition Baesed On Algorithm Of Neural Network Optimized With PSO

Biometrics is a kind of science and technology using individual physiological orbehavioral characteristics to verify identity. It provides a highly reliable and robust approachto the identity recognition. Automatic face detection and recognition is one of the mostattention branches of biomerics and it is also the one of the most active and challenging tasksfor image processing, pattern recognition and computer vision. It is widely applied incommercial and law area, such as mug shots retrieval, real-tine video surveillance in securitysystem and cryptography in bank and so on.The starting point of this paper is to combine the neural network and face recognitiontechnology together and centered on the research of neural network and face recognitionresearch.It aims to solve the problem of face recognition and enhance the recognition systemrecognition ability by ways of neural network and gets higher identification accuracy.First, the research content, approach and development are emphasized. The researchstatus is introduced. The technology of the face detection and recognition are summarized.And the paper describes face preprocessing in detail which is and important step in the facerecognition. The face preprocessing methods we adopt are based on image processingtechniques. The main purpose is to get the standardized facial images, and to eliminate theimpact of illumination to some extent. In this paper, several key preprocessing methods areintroduced, such as geometry normalization, gray-scale normalization and imagesbinary-conversion.Secord, This paper uses the PCA method to the feature extraction of face images.Principal Component Analysis (PCA) as the foundation of the K-L transformation is themost superior in the image compression.By using PCA, the dimension of the input is reducedwhile the main components are maintained. The major idea of PCA is to decompose a dataspace into a linear combination of a smal1collection of bases. In the face-recognitionliterature, the eigenvectors can be referred to as eigenfaces. The probe is identified by firstprojection to all gallery images. We denote a probe. A probe is comparing the projection toall gallery images, and it causes around the compression the mean error to be youngest.In face recognition, this paper focuses on the research of face recognition system based onneural network, that means using the BP neural network to classification and recognition faceimage.BP algorithm was introduced to train the neural network for recognition.Comparingwith the traditional BP algorithm，the PSO algorithm is applied on the optimization of the BPneural networks. The weights and valve values of the neural network are trained with PSO inorder to fast the leaming speed;Finally, a series of experiments were performed on the ORL face image databases. Experiments show that, the neural network can accurately identify the face. And therecognition rate across all trials was higher using PSO-BP neural network than BP neuralnetwork.