Share Email Print
cover

Proceedings Paper

Efficient eye detection using HOG-PCA descriptor
Author(s): Andreas Savakis; Riti Sharma; Mrityunjay Kumar
Format Member Price Non-Member Price
PDF $17.00 $21.00

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);
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)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray