Face Recognition Research Papers 2014 1040

A survey on face detection in the wild

Authors: Stefanos ZafeiriouVisual Information Processing, Department of Computing, Imperial College London, Room: 375, Floor: 3, Huxley Building, South Kensington Campus, London SW7 2AZ, UK
Cha ZhangMicrosoft Research, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399, USA
Zhengyou ZhangMicrosoft Research, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399, USA
Published in:
· Journal
Computer Vision and Image Understanding archive
Volume 138 Issue C, September 2015
Pages 1-24
Elsevier Science Inc.New York, NY, USA
table of contentsdoi>10.1016/j.cviu.2015.03.015
2015 Article
· Citation Count: 9
· Downloads (cumulative): n/a
· Downloads (12 Months): n/a
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boostingdeep neural networksdeformable modelsface detectionfeature extraction

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The Face Recognition (FR) is growing as a major research area because of the broad choice of applications in the fields of commercial and law enforcement. Traditional FR methods based on Visible Spectrum (VS) are facing challenges like object illumination, pose variation, expression changes, and facial disguises. Unfortunately these limitations decrease the performance in object identification and verification. To overcome all these limitations, the Infrared Spectrum (IRS) may be used in human FR. So it leads and encourages the researchers for continuous research in this area of FR. Simultaneously, the present study emphasizes the use of three dimensional cubic dataset i.e. Multi/ Hyperspectral Imagery Data in FR. The IR based Multi/ Hyperspectral Imaging System can minimize the several limitations arise in the existing and classical FR system because the skin spectra derived with cubic dataset depicts the unique features for an individual. Multi/ Hyperspectral Imaging System provides valuable discriminants for individual appearance that cannot be obtained by additional imaging system that's why this may be the future of human FR. This paper also presents a detailed and time to time review of the literature on FR in IRS.

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