Aiming at the One-sidedness of face features or hair features to identify gender in the past, a novel method based on fusion of these features for gender classification was proposed. Face internal features were extracted by Gabor wavelet transform which are robust to the illumination change and scale variations, and feature dimensions were reduced by the method of PCA. Hair region was obtained by the method of dynamic searching in the area of face image. Two kinds of external features of hair length and hair surface area were defined and puted forward the method of corresponding feature extraction. To achieve nonlinear fusion of the three types of features with fuzzy neural network(FNN), the gender classification was completed in the Essex face database and a correct identification rate of 97.1% was obtained.%针对以往仅用人脸特征或头发特征来进行性别分类的片面性，提出了将两类特征相融合的性别分类方法.用对光照、尺度变化具有很强鲁棒性的Gabor小波变换提取人脸内部特征并用PCA方法降维.利用最小代价原理，将动态搜索技术用于图像空间取得头发区域，定义了头发长度、头发表面积两种外部特征，并提出了相应的特征提取方法.采用模糊神经网络对三种特征进行非线性融合.在 Essex 人脸库中进行了性别分类实验，取得了97.1%的准确率.
展开▼
