NDLI: Classifiers comparison for a new eye gaze direction classification system

Content Provider	IEEE Xplore Digital Library
Author	Al-Rahayfeh, A. Faezipour, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of Bridgeport, Bridgeport, CT, USA (Al-Rahayfeh, A.; Faezipour, M.)
Abstract	The classification of eye gaze direction is a growing topic in the field of computer vision research. It has been found to have a wide range of potential applications. However, implementing a robust eye gaze direction classification system is still a challenge which requires more effort. This paper introduces an eye gaze classification algorithm which uses Viola-Jones algorithm for face detection and the Circular Hough Transform for eye detection. Once the eye is detected, low level features, color features in particular, are extracted from the detected eye region. The features are then used in eye gaze direction classification. The performance of different classifiers was evaluated using a database containing 4000 images of 40 males and females from different ages. These classifiers are: K-Nearest Neighbor, Neural Network, Support Vector Machine (SVM) and Decision Tree classifiers. The highest accuracy was obtained when using the linear SVM classifier and is equal to 92.1%.
Sponsorship	IEEE Long Island Sect.
Starting Page	1
Ending Page	6
File Size	2684680
Page Count	6
File Format	PDF
ISBN	9781479938506
DOI	10.1109/LISAT.2014.6845189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transforms Image color analysis Support vector machines Face Classification algorithms Face detection Feature extraction SVM Decision tree eye gaze direction classification nearest neighbors neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Combining multiple SVM classifiers for adult image recognition

Enhanced eye gaze direction classification using a combination of face detection, CHT and SVM

Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection

Eye detection using color information and a new efficient SVM

Detection of Facial Components Based on SVM Classification and Invariant Feature

The Contourlet Transfrom and SVM Classification for Face Recognition

Combining image features for image classification

The impact of image block size on face feature extraction using Discrete Cosine Transform

Automatic image annotation combining SVMS and KNN algorithm

Classifiers comparison for a new eye gaze direction classification system

Similar Documents

Combining multiple SVM classifiers for adult image recognition

Enhanced eye gaze direction classification using a combination of face detection, CHT and SVM

Comparison of neural network, ANFIS, and SVM classifiers for PVC arrhythmia detection

Eye detection using color information and a new efficient SVM

Detection of Facial Components Based on SVM Classification and Invariant Feature

The Contourlet Transfrom and SVM Classification for Face Recognition

Combining image features for image classification

The impact of image block size on face feature extraction using Discrete Cosine Transform

Automatic image annotation combining SVMS and KNN algorithm

Classifiers comparison for a new eye gaze direction classification system