Improving performance of dictionary learning via auxiliary training samples and robust dictionary

Yongbin Qin; Yongjun Zhang; Chengchang Pan; Zhongwei Cui

doi:10.1117/12.2577416

12 October 2020 Improving performance of dictionary learning via auxiliary training samples and robust dictionary

Yongbin Qin, Yongjun Zhang, Chengchang Pan, Zhongwei Cui

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740C (2020) https://doi.org/10.1117/12.2577416
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

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.

Citation Download Citation

Yongbin Qin, Yongjun Zhang, Chengchang Pan, and Zhongwei Cui "Improving performance of dictionary learning via auxiliary training samples and robust dictionary", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740C (12 October 2020); https://doi.org/10.1117/12.2577416

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KEYWORDS

Associative arrays

Detection and tracking algorithms

Chemical species

Facial recognition systems

Databases

Algorithm development

Cognitive modeling

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