Face recognition based on rough set and neural network was proposed for the shortcoming of high dimension of PCA face recognition and low recognition rate for non-training samples.
基于粗糙集和神经网络的人脸识别方法是针对PC A方法中存在的高维数问题和它对未训练过的样本识别率低的缺点而提出的。
The paper presents a method of pose-varied face recognition based on neural network and hierarchical support vector machines.
提出了一种基于神经网络和层次支持向量机的多姿态人脸识别方法。
Use the image illumination correction, reduce the dimension of face images and different lighting conditions, the use of human face images improved the BP neural network for recognition.
运用了图像进行光照校正，人脸图像进行降维及不同的光照条件下的人脸图像运用改进型的BP神经网络对进行识别。
The paper presents an intelligent recognition system of multicharacter fusion, which is acoustic and face based on artificial neural network.
分别提取语音特征和人脸特征作为识别的依据，并用神经网络在特征层上进行融合识别。
The paper presents an intelligent recognition system of multicharacter fusion, which is acoustic and face based on artificial neural network.
分别提取语音特征和人脸特征作为识别的依据，并用神经网络在特征层上进行融合识别。
应用推荐