International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 10, 8 Oct. 2016
Cover page and Table of Contents: PDF (size: 592KB)
Non Intrusive Eye Blink Detection from Low Resolution Images Using HOG-SVM Classifier
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Author(s)
Leo Pauly
1,*
Deepa Sankar
1
Index Terms
Eye blink detection, Fisher Faces, Mean Intensity, HOG features, Artificial Neural Network, SVM classifier
Abstract
Eye blink detection has gained a lot of interest in recent years in the field of Human Computer Interaction (HCI). Research is being conducted all over the world for developing new Natural User Interfaces (NUI) that uses eye blinks as an input. This paper presents a comparison of five non-intrusive methods for eye blink detection for low resolution eye images using different features like mean intensity, Fisher faces and Histogram of Oriented Gradients (HOG) and classifiers like Support Vector Machines (SVM) and Artificial neural network (ANN). A comparative study is performed by varying the number of training images and in uncontrolled lighting conditions with low resolution eye images. The results show that HOG features combined with SVM classifier outperforms all other methods with an accuracy of 85.62% when tested on images taken from a totally unknown dataset.
Cite This Paper
Leo Pauly, Deepa Sankar,"Non Intrusive Eye Blink Detection from Low Resolution Images Using HOG-SVM Classifier", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.10, pp.11-18, 2016. DOI: 10.5815/ijigsp.2016.10.02
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