NDLI: Biologically inspired makeup detection system with application in face recognition

Content Provider	IET Digital Library
Author	Rasti, Sanaz Yazdi, Mehran Shirazi, Mohammad Ali Masnadi
Abstract	The authors propose a novel-makeup detection approach that assists face recognition systems to achieve a higher-accuracy rate while dealing with makeup images. Makeup features are defined in this work using biologically inspired features (BIFs). To establish makeup depictive features more specifically, colour and texture features are essential to be extracted from images. Hence, they create makeup depictive features in the last complex layer of BIFs (C2) as average skin tone (AST) and a histogram of oriented gradient (HOG), where AST is representative of colour and HOG exhibits texture. The proposed makeup BIFs are extracted from grayscale images and instead of breaking the face image into several patches, the whole face image is employed. This resulted in the performance acceleration as well as higher accuracy rate compared with state-of-the-art makeup detection schemes. Subsequently, they employ a machine learning scheme to train the makeup detection system by feeding it makeup and non-makeup labelled images. They exploit the correlation-based method for the face recognition system and compare the results with the direct two-dimensional principal component analysis face recognition scheme for makeup datasets. Experimental results show the highest accuracy rate of 97.07% was achieved by the proposed algorithm for face recognition system considering makeup.
Starting Page	530
Ending Page	535
Page Count	6
ISSN	20474938
Volume Number	7
e-ISSN	20474946
Issue Number	Issue 6, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/7/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5059
Journal	IET Biometrics
Publisher Date	2018-08-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Assists Face Recognition Biologically Inspired Features Colour Computer Vision And Image Processing Technique Face Image Face Recognition Face Recognition System Feature Extraction Grayscale Image Higher Accuracy Rate Higher-accuracy Rate Highest Accuracy Rate HOG Exhibits Texture Image Recognition Image Texture Learning in AI Makeup BIFs Makeup Datasets Makeup Depictive Feature Makeup Detection System Makeup Feature Makeup Image Nonmakeup Labelled Image Novel-makeup Detection Approach Principal Component Analysis State-of-the-art Makeup Detection Scheme Statistics Texture Feature Two-dimensional Principal Component Analysis Face Recognition Scheme
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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