Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

View Researcher's Other Codes

MATLAB code for the paper: “Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition”.

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Meng Yang, Lei Zhang, Simon C. K. Shiu, and David Zhang
Journal/Conference Name IEEE Transactions on Information Forensics and Security
Paper Category
Paper Abstract Local feature based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation based local feature methods. The extensive FR experiments on four large scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-the-art local feature based FR methods.
Date of publication 2012
Code Programming Language MATLAB

Copyright Researcher 2022