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Data Augmentation-Assisted Makeup-Invariant Face Recognition
Joint Authors
Ali, Nouman
Ratyal, Naeem
Sajid, Muhammad
Baig, Mirza Jabbar Aziz
Zafar, Bushra
Dar, Saadat Hanif
Butt, Asif Raza
Shafique, Tamoor
Riaz, Imran
Baig, Shahbaz
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging.
Existing face recognition methods rely on feature extraction and reference reranking to improve the performance.
However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult.
The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces.
The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends.
To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations.
The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup.
We also evaluate the role of partial and full makeup in face images to improve the recognition performance.
The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.
American Psychological Association (APA)
Sajid, Muhammad& Ali, Nouman& Dar, Saadat Hanif& Ratyal, Naeem& Butt, Asif Raza& Zafar, Bushra…[et al.]. 2018. Data Augmentation-Assisted Makeup-Invariant Face Recognition. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1206500
Modern Language Association (MLA)
Sajid, Muhammad…[et al.]. Data Augmentation-Assisted Makeup-Invariant Face Recognition. Mathematical Problems in Engineering No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1206500
American Medical Association (AMA)
Sajid, Muhammad& Ali, Nouman& Dar, Saadat Hanif& Ratyal, Naeem& Butt, Asif Raza& Zafar, Bushra…[et al.]. Data Augmentation-Assisted Makeup-Invariant Face Recognition. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1206500
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206500