Er Meng Joo, Chen Weilong, Wu Shiqian
Computer Control Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639758, Singapore.
IEEE Trans Neural Netw. 2005 May;16(3):679-91. doi: 10.1109/TNN.2005.844909.
In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher's linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating and invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness.
本文提出了一种基于离散余弦变换（DCT）、Fisher线性判别分析（FLD）和径向基函数（RBF）神经网络的高效高速人脸识别方法。首先，利用DCT降低原始人脸图像的维度，并通过舍弃前几个低频DCT系数来减轻大面积光照变化的影响。接下来，使用所提出的聚类算法对截断后的DCT系数向量进行聚类。这一过程使得后续的FLD更加高效。在实施FLD之后，保留了最具判别力和不变性的面部特征，并且训练样本得到了很好的聚类。因此，能够轻松地完成RBF神经网络的进一步参数估计，这有利于RBF神经网络的快速训练。仿真结果表明，所提出的系统具有优异的性能，具有高训练和识别速度、高识别率以及非常好的光照鲁棒性。
