Face recognition with one training image per person
Pattern Recognition Letters
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Handbook of Face Recognition
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Selecting discriminant eigenfaces by using binary feature selection
Neural Computing and Applications
Pose invariant face recognition based on hybrid-global linear regression
Neural Computing and Applications
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition technique using symbolic linear discriminant analysis method
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Eigenface recognition technique reserves limitations in achieving good performance. This includes the large-scale database required, its sensitivity to improper illumination, as well as to different expressions of a human face, and image background. This paper presents an efficient and accessible solution for some of these limitations by improving the database's design. Face recognition accuracy has been enhanced via the inclusion of a modified version of the images in the database. Illumination and various face positions have been integrated into the already available small-scale database. Recognition is sensitive to the illuminated side of a face under consideration. Applying the proposed approach and choosing proper pre-processing values, has improved the system's performance.