Open AccessProceedings Article
A novel eye detection algorithm utilizing edge-related geometrical information
Stylianos Asteriadis,Nikos Nikolaidis,Andras Hajdu,Ioannis Pitas +3 more
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TLDR
A novel method for eye detection and eye center localization, based on geometrical information is described, which can work on low-resolution images and has been tested on two face databases with very good results.Citations
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Hybrid method based on topography for robust detection of iris center and eye corners
TL;DR: The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images and shows that the suggested algorithm is robust and accurate.
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Facial feature detection using distance vector fields
TL;DR: A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information, which has been tested on the XM2VTS and BioID databases, with very good results.
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Eye detection using discriminatory Haar features and a new efficient SVM
Shuo Chen,Chengjun Liu +1 more
TL;DR: An accurate and efficient eye detection method using the discriminatory Haar features (DHFs) and the eSVM and a new efficient support vector machine (eSVM) to improve the efficiency of the SVM is presented.
Proceedings Article
Human eye localization using edge projections
TL;DR: A human eye localization algorithm in images and video is presented for faces with frontal pose and upright orientation and it is experimentally observed that this method provides promising results for both image and video processing applications.
Journal ArticleDOI
Precise localization of eye centers in low resolution color images
TL;DR: This paper introduces an automatic, non-intrusive method for precise eye center localization in low resolution images, acquired from single low-cost cameras, that uses color information to derive a novel eye map that emphasizes the iris area and a radial symmetry transform which operates both on the original eye images and the eye map.
References
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Journal ArticleDOI
Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.