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Redundant DWT based translation invariant wavelet feature extraction for face recognition

机译:基于冗余小波变换的平移不变小波特征提取用于人脸识别

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Discrete Wavelet Transform (DWT) is sensitive to the translation/shift of input signals, so its effectiveness could be negatively impacted when we encounter translation among signals. To deal with such drawbacks, this paper proposes redundant DWT(RDWT) based method to achieve image registration, translation invariant wavelet feature extraction and face recognition. We select a representative face from each person to form a reference face set and perform DWT on it. For each test face, we perform RDWT and compare its redundant horizontal and vertical details with the corresponding details obtained from the reference face. The reference face that is the most similar to the test face is determined to be the recognized face. Experiments on Yaleface database prove the effectiveness of our RDWT based method.
机译:离散小波变换(DWT)对输入信号的平移/移位敏感,因此当我们在信号之间进行平移时,其有效性可能受到负面影响。针对这种缺陷,本文提出了一种基于冗余DWT(RDWT)的图像配准,平移不变小波特征提取和人脸识别的方法。我们从每个人中选择一张具有代表性的面孔,以形成一个参考面孔集并对其进行DWT。对于每个测试面,我们执行RDWT并将其多余的水平和垂直细节与从参考面获得的相应细节进行比较。与测试脸部最相似的参考脸部被确定为识别出的脸部。在Yaleface数据库上进行的实验证明了我们基于RDWT的方法的有效性。

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