Version 1
: Received: 26 May 2020 / Approved: 27 May 2020 / Online: 27 May 2020 (12:07:19 CEST)
How to cite:
Truong, H.P.; Nguyen-Khoa, M.B.; Kim, Y. Bilateral Line Local Binary Patterns for Face Recognition. Preprints2020, 2020050451. https://doi.org/10.20944/preprints202005.0451.v1
Truong, H.P.; Nguyen-Khoa, M.B.; Kim, Y. Bilateral Line Local Binary Patterns for Face Recognition. Preprints 2020, 2020050451. https://doi.org/10.20944/preprints202005.0451.v1
Truong, H.P.; Nguyen-Khoa, M.B.; Kim, Y. Bilateral Line Local Binary Patterns for Face Recognition. Preprints2020, 2020050451. https://doi.org/10.20944/preprints202005.0451.v1
APA Style
Truong, H.P., Nguyen-Khoa, M.B., & Kim, Y. (2020). Bilateral Line Local Binary Patterns for Face Recognition. Preprints. https://doi.org/10.20944/preprints202005.0451.v1
Chicago/Turabian Style
Truong, H.P., Minh Bao Nguyen-Khoa and Yong-Guk Kim. 2020 "Bilateral Line Local Binary Patterns for Face Recognition" Preprints. https://doi.org/10.20944/preprints202005.0451.v1
Abstract
Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.
Keywords
Bilateral Line Local Binary Patterns; Facial matrix; Statistical subspace; Face recognition; Calibrated SVM model; Ensemble learning
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
