Open Access

ARTICLE

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

by Masoud Muhammed Hassan^1,*, Haval Ismael Hussein¹, Adel Sabry Eesa¹, Ramadhan J. Mstafa^1,2

1 Department of Computer Science, Faculty of Science, University of Zakho, Duhok, 42002, Kurdistan Region, Iraq
2 Scientific Research and Development Center, Nawroz University, Duhok, 42001, Kurdistan Region, Iraq

* Corresponding Author: Masoud Muhammed Hassan. Email:

Computers, Materials & Continua 2021, 68(2), 1637-1659. https://doi.org/10.32604/cmc.2021.016467

Received 03 January 2021; Accepted 14 February 2021; Issue published 13 April 2021

Abstract

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.

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Citations