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首页> 外文会议>2015 IEEE International Conference on Computer Graphics, Vision and Information Security >Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis
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Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis

机译：Gabor小波与线性判别分析相结合的混合人脸识别方法

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Face Recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a Hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-NearestNeighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.
机译：人脸识别系统在监视和人机交互系统中找到了许多应用。由于使用面部识别系统的应用非常重要，并且要求更高的准确性，因此期望面部识别系统中的鲁棒性更高，计算时间更少。本文提出了一种结合了Gabor小波和线性判别分析（HGWLDA）的人脸识别混合方法。近似归一化的输入灰度图像并缩小其尺寸，以降低Gabor滤波器的处理开销。该图像与一组具有不同比例和方向的Gabor滤波器卷积。 LDA是一种子空间分析技术，用于减少类内空间并最大化类间空间。使用的技术是二维线性判别分析（2D-LDA），二维双向LDA（（2D）2LDA），加权二维双向线性判别分析（Wt（2D）2 LDA）。 LDA通过提取差异较大的特征来减小特征维。 k-NearestNeighbour（k-NN）分类器用于通过比较测试图像的特征和每个训练集特征来分类和识别测试图像。 HGWLDA方法在光照条件下具有鲁棒性，因为Gabor特征是光照不变的。该方法还旨在通过使用较少数量的特征来实现不同的表情来提高识别率。使用AT＆T数据库，MIT-India人脸数据库和faces94数据库评估了拟议的HGWLDA方法的性能。发现所提出的HGWLDA方法比现有的Gabor方法提供更好的结果。

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