NDLI: Combining rectangular and triangular image regions to perform real-time face detection

Content Provider	IEEE Xplore Digital Library
Author	Proenca, H. Filipe, S.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. da Beira Interior, Covilha (Proenca, H.; Filipe, S.)
Abstract	Nowadays, face detection techniques assume growing relevance in a wide range of applications (e.g., biometrics and automatic surveillance)and constitute a pre-requisite of many image processing stages. Among a large number of published approaches, one of the most relevant is the method proposed by Viola and Jones to perform real-time face detection through a cascade schema of weak classifiers that act together to compose a strong and robust classifier. This method was the basis of our work and motivated the key contributions given in this paper. At first, based on the computer graphics concept of ldquotriangle meshrdquo we propose the notion of ldquotriangular integral featurerdquo to describe and model face properties. Also, we show results of our face detection experiments that point to an increase of the detection accuracy when the triangular features are mixed with the rectangular in the candidate feature set, which is considered an achievement. Also, it should be stressed that this optimization is obtained without any relevant increase in the computational requirements, either spatial or temporal, of the detection method.
Starting Page	903
Ending Page	908
File Size	241922
Page Count	6
File Format	PDF
ISBN	9781424421787
DOI	10.1109/ICOSP.2008.4697274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Humans Object detection Computer graphics Pattern recognition Feature extraction Optimization methods Surface morphology Probability Computer science Object Detection Face Detection Feature Extraction
Content Type	Text
Resource Type	Article

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