Title: Makeup-invariant face identification and verification using fisher linear discriminant analysis-based Gabor filter bank and histogram of oriented gradients

Authors: Ismaila A. Kamil; Aliu S. Are

Addresses: Department of Electrical & Electronic Engineering, University of Ibadan, Ibadan, Nigeria ' Department of Electrical & Electronic Engineering, University of Ibadan, Ibadan, Nigeria

Abstract: Non-permanent facial makeup is one of the most difficult problems inhibiting face recognition systems in security applications. In this paper, a new method is proposed for makeup-invariant face identification and verification. Face images from the virtual makeup (VMU) and YouTubemakeup (YMU) datasets were subjected to the Gabor filtering and histogram of oriented gradients (HOG) methods for feature extraction. The Gabor and HOG features were concatenated to generate the final feature vectors and subsequently reduced using the fisher linear discriminant analysis subspace. The reduced features were classified using the city block distance (CBD), Euclidean distance (EUC), cosine similarity measure (CSM) and whitened cosine similarity measure (WCSM). The CSM achieved the best recognition rates out of the four metrics used. Performance evaluation of these metrics produced identification and verification rates of 100% and 100% for the VMU database, and 72.52% and 79.47% for the YMU database, respectively. The developed method outperformed several state-of-the-art methods initially exploited.

Keywords: city block distance; cosine similarity measure; euclidean distance; face identification; face verification; fisher linear discriminant analysis; Gabor filter bank; histogram of oriented gradients; non-permanent facial makeup; whitened cosine similarity measure.

DOI: 10.1504/IJSISE.2017.087768

International Journal of Signal and Imaging Systems Engineering, 2017 Vol.10 No.5, pp.257 - 270

Received: 10 Nov 2016
Accepted: 26 Jun 2017
Published online: 01 Nov 2017 *

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