Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP | IEEE Conference Publication | IEEE Xplore

Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP


Abstract:

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method based on deep lea...Show More

Abstract:

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method based on deep learning to solve the adverse impact imposed by illumination variation in the face recognition process. Firstly, illumination preprocessing is applied to improve the adverse effects of intense illumination changes on face images. Secondly, the Log-Gabor filter is used to obtain the Log-Gabor feature images of different scales and directions, then, LBP (Local Binary Pattern) features of images subblock is extracted. Lastly, texture feature histograms are formed and input into the deep belief network (DBN) visual layer, then face classification and recognition are completed through deep learning in DBN. Experimental results show that superior performance can be obtained in the developed approach by comparisons with some state-of-the-arts.
Date of Conference: 15-17 March 2019
Date Added to IEEE Xplore: 06 June 2019
ISBN Information:
Conference Location: Chengdu, China

I. Introduction

Face recognition has been a research hotspot in pattern recognition and image processing in the past few years due to its friendliness and convenience. In recent years, many algorithms have been proposed by scholars [1] –[4]. Most previous researches can obtain satisfying recognition performance under uniform illumination conditions with frontal face images. However, it is still a very challenging research area because even the images of same person seem different due to occlusion, illumination, expression and pose variation, which can cause sharp decline in recognition rate [5, 6]. Improvement of face recognition performance in complex light environment is still a difficult problem in the field of artificial intelligence and computer vision.

References

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