Sequential three-way decision and granulation for cost-sensitive face recognition

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• 作者：Huaxiong Li^a ; ^{huaxiongli@nju.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Libo Zhang^a ; ^{mg1315013@smail.nju.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Bing Huang^b ; ^{hbhuangbing@126.com" class="auth_mail" title="E-mail the corresponding author} ; Xianzhong Zhou^a ; ^{zhouxz@nju.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Three-way decisions ; Decision-theoretic rough sets ; Granular computing ; Cost-sensitive learning ; Face recognition
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：91
• 期：Complete
• 页码：241-251
• 全文大小：1120 K

文摘

Many previous studies on face recognition attempted to seek a precise classifier to achieve a low misclassification error, which is based on an assumption that all misclassification costs are the same. In many real-world scenarios, however, this assumption is not reasonable due to the imbalanced misclassification cost and insufficient high-quality facial image information. To address this issue, we propose a sequential three-way decision method for cost-sensitive face recognition. The proposed method is based on a formal description of granular computing. It develops a sequential strategy in a decision process. In each decision step, it seeks a decision which minimizes the misclassification cost rather than misclassification error, and it incorporates the boundary decision into the decision set such that a delayed decision can be made if available high-quality facial image information is insufficient for a precise decision. To describe the granular information of the facial image in three-way decision steps, we develop a series of image granulation methods based on two-dimensional subspace projection methods including 2DPCA, 2DLDA and 2DLPP. The sequential three-way decisions and granulation methods present an applicable simulation on human decisions in face recognition, which simulate a sequential decision strategy from rough granule to precise granule. The experiments were conducted on two popular facial image database, which validated the effectiveness of the proposed methods.