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Hassanpour, Hamid, Kohansal, Omid, Asadi Amiri, Sekineh. (1397). Robust Face Recognition Under Illumination Changes and Pose Variations. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 5(2), 81-89. doi: 10.22108/jcs.2018.110882.
Hamid Hassanpour; Omid Kohansal; Sekineh Asadi Amiri. "Robust Face Recognition Under Illumination Changes and Pose Variations". سامانه مدیریت نشریات علمی دانشگاه اصفهان, 5, 2, 1397, 81-89. doi: 10.22108/jcs.2018.110882.
Hassanpour, Hamid, Kohansal, Omid, Asadi Amiri, Sekineh. (1397). 'Robust Face Recognition Under Illumination Changes and Pose Variations', سامانه مدیریت نشریات علمی دانشگاه اصفهان, 5(2), pp. 81-89. doi: 10.22108/jcs.2018.110882.
Hassanpour, Hamid, Kohansal, Omid, Asadi Amiri, Sekineh. Robust Face Recognition Under Illumination Changes and Pose Variations. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 1397; 5(2): 81-89. doi: 10.22108/jcs.2018.110882.

Robust Face Recognition Under Illumination Changes and Pose Variations

Journal of Computing and Security
مقاله 4، دوره 5، شماره 2، مهر 2018، صفحه 81-89    اصل مقاله (923.44 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2018.110882.
نویسندگان
Hamid Hassanpour1؛ Omid Kohansal2؛ Sekineh Asadi Amiri* 3
1Prof ., Faculty of Computer Engineering & IT, Shahrood University of Technology, Shahrood, Iran.
2Faculty of Computer Engineering & IT, Shahrood University of Technology, Prof. Shahrood, Iran.
3Assistant professor, Facaulty of Technology and Engineering, University of Mazandaran, Babolsar, Iran.
چکیده
There are many applications for face recognition. Due to illumination changes, and pose variations of facial images, face recognition is often a challenging and a complicated process. In this paper, we propose an effective and robust face recognition method. Firstly, we select those areas from the face (such as eyes, nose, and mouth), which are more informative in face recognition. Then SIFT (Scale Invariant Feature Transform) descriptor is utilized for feature extraction from the selected areas. SIFT descriptor detects keypoints in the image and describes each keypoint with a feature vector with length 128. To speed up the proposed method, PCA (Principal Component Analysis) is applied on the SIFT feature vector to reduce the vector’s length. Finally, Kepenekci matching method is used to assess similarity between the images. The proposed method is evaluated on the ORL, Extended Yale B, and FEI databases. Results show considerable performance of the proposed face recognition method in comparison with several state-of-the-arts.
کلیدواژه‌ها
Face Recognition؛ PCA؛ SIFT Descriptor؛ Feature Extraction
سایر فایل های مرتبط با مقاله
مراجع
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