Robust Face Recognition Against Expressions and Partial Occlusions

• Published in: International journal of automation and computing Vol. 13; no. 4; pp. 319 - 337
• Main Authors: Zaman, Fadhlan Kamaru, Shafie, Amir Akramin, Mustafah, Yasir Mohd
• Format: Journal Article
• Language: English
• Published: Beijing Institute of Automation, Chinese Academy of Sciences 01.08.2016; Springer Nature B.V
• Subjects: CAE) and Design; Computer Applications; Computer-Aided Engineering (CAD; Control; Discriminant analysis; Engineering; Face recognition; Facial recognition technology; Feature recognition; Feature selection; Human performance; Independent component analysis; Mechatronics; Research Article; Robotics; Strategy; 人脸识别; 局部线性嵌入; 独立成分分析; 线性判别分析; 部分遮挡; 面部特征; 面部表情; 鲁棒; dimensionality reduction; Face recognition; single sample; feature selection; facial expressions
• ISSN: 1476-8186; 2153-182X; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-016-0974-6

Facial features under variant-expressions and partial occlusions could have degrading effect on overall face recognition performance. As a solution, we suggest that the contribution of these features on final classification should be determined. In order to represent facial features＇ contribution according to their variations, we propose a feature selection process that describes facial features as local independent component analysis （ICA） features. These local features are acquired using locally lateral subspace （LLS） strategy. Then, through linear discriminant analysis （LDA） we investigate the intraclass and interclass representation of each local ICA feature and express each feature＇s contribution via a weighting process. Using these weights, we define the contribution of each feature at local classifier level. In order to recognize faces under single sample constraint, we implement LLS strategy on locally linear embedding （LLE） along with the proposed feature selection. Additionally, we highlight the efficiency of the implementation of LLS strategy. The overall accuracy achieved by our approach on datasets with different facial expressions and partial occlusions such as AR, JAFFE, FERET and CK% is 90.70%. We present together in this paper survey results on face recognition performance and physiological feature selection performed by human subjects.