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Dynamic Feature Subset Selection for Occluded Face Recognition

Najlaa Hindi Alsaedi*, Emad Sami Jaha

Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, KSA

* Corresponding Author: Najlaa Hindi Alsaedi. Email: email

Intelligent Automation & Soft Computing 2022, 31(1), 407-427.


Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.


Cite This Article

APA Style
Alsaedi, N.H., Jaha, E.S. (2022). Dynamic feature subset selection for occluded face recognition. Intelligent Automation & Soft Computing, 31(1), 407-427.
Vancouver Style
Alsaedi NH, Jaha ES. Dynamic feature subset selection for occluded face recognition. Intell Automat Soft Comput . 2022;31(1):407-427
IEEE Style
N.H. Alsaedi and E.S. Jaha, "Dynamic Feature Subset Selection for Occluded Face Recognition," Intell. Automat. Soft Comput. , vol. 31, no. 1, pp. 407-427. 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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