Kim, Donghoom and Dahyot, Rozenn (2008) Face components detection using SURF descriptor and SVMs. 2008 International Machine Vision and Image Processing Conference.

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Abstract

We present a feature-based method to classify salient points as belonging to objects in the face or background classes. We use SURF local descriptors (speeded up robust features) to generate feature vectors and use SVMs (support vector machines) as classifiers. Our system consists of a two-layer hierarchy of SVMs classifiers. On the first layer, a single classifier checks whether feature vectors are from face images or not. On the second layer, component labeling is operated using each component classifier of eye, mouth, and nose. This approach has the advantage about operating time because windows scanning procedure is not needed. Finally, this system performs the procedure to apply geometrical constraints to labeled descriptors. We show experimentally the efficiency of our approach.

Item Type:	Article
Keywords:	Face Components; Detection; SURF Descriptors; SVMs;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15287
Identification Number:	https://doi.org/10.1109/IMVIP.2008.15
Depositing User:	Rozenn Dahyot
Date Deposited:	19 Jan 2022 13:09
Journal or Publication Title:	2008 International Machine Vision and Image Processing Conference
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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