Automatic Face Recognition, In Virtual Reality

Face Recognition(FR) is a offshoot of Pattern Recognition. It involves several fields such as Image Process, Computer Vision and Artificial Intelligence. Compared with fingerprint recognition, it is more convenient to get samples. It is applied widely such as security certification in companies or banks which require high security, recognizing suspects in police office.Face Recognition began to develop in 1960s and it has gained great achievements. It can be separated into three steps: face localization, feature extraction, recognition. There are three main methods in face localization: localization according to face outline, localization according to complexion and localization according to templates constructed by some standard sample images. As to feature extraction, it can be divided into two parts because face features can be divided into geometrical features and algebraic features. While extracting geometrical features, the features of eyes, nose, mouth, eyebrows can be gained by some image processes: binary, sharpen, smooth, projection, calculating gradient and so on. In order to extract algebraic features, we can do some mathematical transformation for the digital images such as singular value decomposition, K-L transformation. In third step, we can judge the resemblance between input image and images in database according the distance between them. Methods of neural network can also be applied into face recognition.In this paper we proposed a method for face localization which takes advantage of pixel distribution features of face Images. While locating the face inner outline, the distribution features of both binary image of the face and greyjevel image of the face are introduced. Finally we can get standardization face Images with satisfactory effect.We use PCA(the Principal Component Analysis) for feature extractionrecognition, which has been proved to be the top advanced technology of face recognition. First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. Then every images in database can be projected into the feature matrix and gain a projection vector, so does the input image. Then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors. We have used sample images to test the face recognition application based the methods above, and the recognition rate is above 85%.