计算机科学
人工智能
词典学习
机器学习
模式识别(心理学)
K-SVD公司
培训(气象学)
任务(项目管理)
深度学习
作者
Yongbin Qin,Yongjun Zhang,Chengchang Pan,Zhongwei Cui
出处
期刊:International Symposium on Artificial Intelligence
日期:2020-10-12
卷期号:11574
摘要
A key to dictionary learning is to attain a robust dictionary, which enables difference between test samples and training samples of the same class to be alleviated. Owing to this factor, the dictionary can bring proper representations of test samples and produce better classification results for them. For face recognition, because of varying facial appearance caused by changeable illuminations, poses and facial expressions, a robust dictionary is definitely preferred. In this paper, we propose a robust dictionary learning method for face recognition. Robustness is attained in a two-fold way. First, auxiliary faces are produced via original face images. Second, the scheme to attain the dictionary under the condition that label coefficients can deviate from sample coefficients is designed. Auxiliary faces express possible variations of faces. Moreover, it seems that difference between auxiliary faces and original training samples of the same class somewhat reflects difference between test samples and training samples, thus use of auxiliary faces is beneficial to improve robustness of the method. The scheme to attain the dictionary further enhances robustness.
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