-> Objective of this project was to develop a fast and robust face recognition system that is invariant to changes such as illumination, expressions and occlusions by exploring features in image - space, scale and orientation.
-> Algorithm was based on Local Gabor binary pattern histogram sequence (LGBPHS) for improved feature matching and efficiency.
-> Used MATLAB with Image Processing Tool Box to implement the project.
https://github.com/vedantbhoj/Security-based-Face-Recognition/blob/master/Project%20Report.pdf
- Zhen Lei, Shengcai Liao, Matti Pietikäinen, “Face Recognition by Exploring Information Jointly in Space, Scale and Orientation” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 1, JANUARY 2011.
- Shufu Xie, Shiguang Shan, Xilin Chen, Jie Chen, “Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 5, MAY 2010.
- W. C. Zhang, S. G. Shan, W. Gao, and H. M. Zhang, “Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition” IEEE Transaction 2005.
- “Handbook of face recognition”, Li S.Z., Jain A. (Springer, 2005).
- Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 6, JUNE 2010.
- B. Zhang, S. Shan, X. Chen, and W. Gao, “Histogram of gabor phase patterns (hgpp): A novel object representation approach for face recognition” IEEE Trans. Image Process., vol. 16, no. 1, pp. 57–68, Jan. 2007.