NDLI: Automatic adaptation of SIFT for robust facial recognition in uncontrolled lighting conditions

Content Provider	IET Digital Library
Author	Mahamdioua, Meriama Benmohammed, Mohamed
Abstract	The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a powerful method that extracts and describes local features called keypoints from images. These keypoints are invariant to scale, translation, and rotation, and partially invariant to image illumination variation. Despite their robustness against these variations, strong lighting variation is a difficult challenge for SIFT-based facial recognition systems, where significant degradation of performance has been reported. To develop a robust system under these conditions, variation in lighting must be first eliminated. Additionally, SIFT parameter default values that remove unstable keypoints and inadequately matched keypoints are not well-suited to images with illumination variation. SIFT keypoints can also be incorrectly matched when using the original SIFT matching method. To overcome this issue, the authors propose propose a method for removing the illumination variation in images and correctly setting SIFT's main parameter values (contrast threshold, curvature threshold, and match threshold) to enhance SIFT feature extraction and matching. The proposed method is based on an estimation of comparative image lighting quality, which is evaluated through an automatic estimation of gamma correction value. Through facial recognition experiments, the authors find significant results that clearly illustrate the importance of the proposed robust recognition system.
Starting Page	623
Ending Page	633
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 5, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0190
Journal	IET Computer Vision
Publisher Date	2018-02-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Comparative Image Lighting Quality Estimation Computer Vision And Image Processing Technique Estimation Theory Face Recognition Feature Extraction Gamma Correction Value Automatic Estimation Illumination Variation Image Illumination Variation Image Matching Image Recognition Integral Transforms Local Feature Extraction Robust Facial Recognition Scale Invariant Feature Transform SIFT Automatic Adaptation SIFT Feature Matching SIFT Matching Method SIFT Parameter Default Value SIFT-based Facial Recognition System Statistics Transform Uncontrolled Lighting Condition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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