论文标题
通过神经网分类器面对识别
Face identification by means of a neural net classifier
论文作者
论文摘要
本文描述了一种新颖的面部识别方法，该方法将特征性理论与神经网络结合在一起。我们使用特征法的方法来降低输入图像的维度，以及执行识别过程的神经网络分类器。提出的方法识别面部表达，面部细节和照明条件的变化。已经达到了超过87％的识别率，而Turk和Pentland的经典方法则达到75.5％。
This paper describes a novel face identification method that combines the eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in order to reduce the dimensionality of the input image, and a neural net classifier that performs the identification process. The method presented recognizes faces in the presence of variations in facial expression, facial details and lighting conditions. A recognition rate of more than 87% has been achieved, while the classical method of Turk and Pentland achieves a 75.5%.
