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Face recognition using enhanced energy of Discrete Wavelet Transform

机译:利用增强的离散小波变换能量进行人脸识别

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The face recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses etc.), etc. In this paper multi scale technique Discrete Wavelet Transform is used for preprocessing. The complexity is reduced by reducing the size of the image to 1 by 4. Discrete Wavelet Transform is applied to face images and divided into four blocks and the energy of each block is calculated. Block energy is maximized and enhanced image is obtained. In this paper K Means clustering algorithm is used to cluster the pixels in face image obtained from preprocessing step. Binary threshold is applied in the clusters. The performance of the proposed method is tested using Fuzzy K Nearest Neighbour classifier and face recognition accuracy rate is computed. Testing is done using ORL face database.
机译:由于头部旋转和倾斜,照明强度和角度,面部表情,衰老,部分遮挡(例如,戴着帽子,围巾,眼镜等)的巨大差异,使得面部识别问题变得十分困难。本文采用多尺度技术小波变换用于预处理。通过将图像的大小减小为1乘4,可以降低复杂度。将离散小波变换应用于人脸图像并分成四个块,并计算每个块的能量。块能量最大化并获得增强的图像。本文采用K均值聚类算法对从预处理步骤获得的人脸图像中的像素进行聚类。二进制阈值应用于群集。使用模糊K最近邻分类器对所提方法的性能进行了测试,计算了人脸识别的准确率。使用ORL人脸数据库进行测试。

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