Face Image Recognition Algorithm based on Singular Value Decomposition

Jia Tang, Lin Cui, Zhenggao Pan, Chengfang Tan, Shanshan Li, Weijie Wang
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Abstract

In face image recognition, features play a decisive role in recognition and classification. Feature extraction can describe the image, but the extracted data may contain redundant and useless information, which affects the model generalization learning. For these problems, a face image recognition algorithm with singular value decomposition is proposed. The original data is firstly decomposed by singular value decomposition with SVD algorithm, and then the k values in the top of the singular value are selected and calculated to obtain the sample information after attribute reduction, and then the result of the reduced data is classified by the model using the extreme learning machine algorithm, and finally the type corresponding to the image can be predicted by the model, and the experimental results on the ORL face image data set also prove that algorithm has a good recognition efficiency.
基于奇异值分解的人脸图像识别算法
在人脸图像识别中,特征在识别和分类中起着决定性的作用。特征提取可以描述图像,但提取的数据可能包含冗余和无用的信息,影响模型的泛化学习。针对这些问题,提出了一种奇异值分解的人脸图像识别算法。首先用SVD算法对原始数据进行奇异值分解分解,然后选择并计算奇异值顶部的k值,得到属性约简后的样本信息,然后用极限学习机算法对约简后的数据结果进行模型分类,最后通过模型预测图像对应的类型。在ORL人脸图像数据集上的实验结果也证明了该算法具有良好的识别效率。
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