This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (FFC), for face recognition. On the assumption that FFC receives single-scale Gabor filters of varying orientations as input, these filters are self-cross convolved by fold to instantiate a filter offspring set. The FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.
|Number of pages||15|
|Journal||IEEE Transactions on Circuits and Systems for Video Technology|
|Publication status||Published - 2019 Jan|
Bibliographical noteFunding Information:
Manuscript received April 24, 2016; revised August 29, 2016 and October 9, 2016; accepted September 27, 2017. Date of publication October 10, 2017; date of current version January 7, 2019. This work was supported by the National Research Foundation of Korea through the Korean Government (MSIP) under Grant 2016R1A2B4011656. This paper was recommended by Associate Editor X. Wang. (Corresponding author: Andrew Beng-Jin Teoh.) The authors are with the School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Media Technology
- Electrical and Electronic Engineering