Novel Method Fusing （2D）^2 LDA with Multichannel Model for Face Recognition

A fusion method of Gabor features and (2D)~2LDA for face feature extraction is proposed in this paper. Gabor filters are utilized to extract multi-direction and multi-scale features from facial image to employ its robust performance for illumination,expressional variability and other factors. The extracted features have the defect of high dimension and redundancy data.(2D)~2LDA is implemented to reduce the dimension of Gabor features and select effective feature data. Finally, the nearest neighbor classifier is used to classify characteristics and complete face recognition. The experiments are implemented by using ORL database and Yale database respectively. The experimental results show that the proposed method significantly reduces the dimension of Gabor features and decrease the influence of other factors. The proposed method acquires excellent recognition accuracy and has light architectures as well.

（2D）2LDA和支持向量机在PCB焊点检测中的应用

在现代印刷电路板生产过程中,焊点质量检测是其中的一个重要组成部分。然而随着电子元器件封装的不断小型化,焊点质量检测也变得越来越具有挑战性,传统的依靠人工检测的方式已经越来越不能满足实际生产的需要,同时,基于图像处理与人工智能的自动光学检测技术在焊点质量检测中得到越来越广泛的应用。在此提出一种将双方向二维线性判别分析和支持向量机相结合的焊点质量自动光学检测方法。实验结果表明,该方法能够取得良好的识别效果。

基于行列特征复融合的人脸识别

针对基于行列投影特征融合的二维线性判别分析中存在的问题,提出一种行列特征复融合的人脸识别算法。通过二维线性判别分析获得行和列的特征矩阵融合成一个复特征矩阵,从复特征矩阵重提取最具分类能力的系数组成特征向量。利用AT&T和AR人脸数据库对该算法进行性能测试,结果表明该算法具有较高的识别率。