一种基于相关性投影的人脸性别分类算法

A face gender classification algorithm based on correlation projection

针对于人脸图像检测的有效利用性,为了提高其检测的性能,提出一种新的基于监督学习的优化相关性投影(ORP)人脸性别分类算法,并将其应用到基于Eigenface算法与Fisherface算法的人脸识别中,以及应用WPCA到基于PGA的性别分类中。本文算法首先基于带权主成分分析(WPCA)算法来降低脸部维度,将脸部特征提取出;然后,对其进行优化,同时计算ORP的误差函数;最后,最小化脸部ORP误差函数,计算特征向量的欧式距离,进行人脸性别分类。将提出方法与传统方法进行对比,在FERET数据库上进行了实验,证明了本文方法的有效性,获得了优于传统方法的识别率。

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.