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Proceedings Paper

Efficient eye detection using HOG-PCA descriptor
Author(s): Andreas Savakis; Riti Sharma; Mrityunjay Kumar
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Paper Abstract

Eye detection is becoming increasingly important for mobile interfaces and human computer interaction. In this paper, we present an efficient eye detector based on HOG-PCA features obtained by performing Principal Component Analysis (PCA) on Histogram of Oriented Gradients (HOG). The Histogram of Oriented Gradients is a dense descriptor computed on overlapping blocks along a grid of cells over regions of interest. The HOG-PCA offers an efficient feature for eye detection by applying PCA on the HOG vectors extracted from image patches corresponding to a sliding window. The HOG-PCA descriptor significantly reduces feature dimensionality compared to the dimensionality of the original HOG feature or the eye image region. Additionally, we introduce the HOG-RP descriptor by utilizing Random Projections as an alternative to PCA for reducing the dimensionality of HOG features. We develop robust eye detectors by utilizing HOG-PCA and HOG-RP features of image patches to train a Support Vector Machine (SVM) classifier. Testing is performed on eye images extracted from the FERET and BioID databases.

Paper Details

Date Published: 3 March 2014
PDF: 8 pages
Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270J (3 March 2014); doi: 10.1117/12.2036824
Show Author Affiliations
Andreas Savakis, Rochester Institute of Technology (United States)
Riti Sharma, Rochester Institute of Technology (United States)
Mrityunjay Kumar, FiveFocal (United States)


Published in SPIE Proceedings Vol. 9027:
Imaging and Multimedia Analytics in a Web and Mobile World 2014
Qian Lin; Jan Philip Allebach; Zhigang Fan, Editor(s)

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