Abstract: BP neural network is a relatively mature solution to face orientation discrimination of a single image. Put forward a new eigenvalue extraction method on the basis of previous studies. Firstly, detect the boundary of the image and make it binary to produce a 0-1 matrix corresponding to pixels. I}hen, obtain the data related to the surrounding areas of eyes by splitting the matrix. Considering the specialty of face image, that is, the interference from the data on side bums,split the matrix again. Secondly, extract two types of eigenvalue. One is the distribution discrete degree of elements whose value is 1; the other is their average distribution position. Finally, construct the neural network by taking a 2-dimention vector of the two eigenvalucs as input and 5 types of face orientation as output. The correct recognition rate of the network is 100%. Comparing to eigenvaluc extraction by PCA, this way is more comprehensible because eigenvalue has more significance in practice. There is no need to find out eye position, so this way is more succinct than geometric methods.

Key words: Discrimination analysis,BP neural networks,Discrete degree,Face orientation discrimination