3 March 2014 Efficient eye detection using HOG-PCA descriptor
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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.
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Andreas Savakis, Riti Sharma, Mrityunjay Kumar, "Efficient eye detection using HOG-PCA descriptor", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270J (3 March 2014); doi: 10.1117/12.2036824; https://doi.org/10.1117/12.2036824
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