According to the effective use of face image detection,in order to improve the performance of the existing face detection algorithms,a new method based on supervised learning for face gender class ification of relevance maps is proposed.This method can be applied to different facial analysis tasks,and has been successf ully applied to face recognition based on Eigenface algorithm and Fisherface algorithm,also applied to the principal component analysis (PGA) in t he gender classification.Firstly,the algorithm reduces the dimension of the face based on the weighted PCA algorithm,and facial features are extracted.Then,the algorith m is optimized and the error function of correlation projection is calculated.Finally,the correlation error function in facial projection is mini mized and the Euclidean distance of the feature vector is calculated for face gender classification.The proposed method is compared with other feat ure extraction methods,and the corresponding experiments are carried out on the FERET database.The results prove the method is efficient and can obtain h igher recognition accuracy than traditional methods.