Automatic eye detection using intensity filtering and K-means clustering

Authors:
Zhiming Qian;Dan Xu
Affiliations:
Chuxiong Normal University, Chuxiong 675000, China and Department of Computer Science and Engineering, Yunnan University, Kunming 650091, China;Department of Computer Science and Engineering, Yunnan University, Kunming 650091, China
Venue:
Pattern Recognition Letters
Year:
2010

Citing 17
Cited 2

Image Representation Using 2D Gabor Wavelets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
FloatBoost Learning and Statistical Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Real Time Eye Detection in Face Images

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Overview of the Face Recognition Grand Challenge

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automatic Eye Detection and Its Validation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A robust eye detection method using combined binary edge and intensity information

Pattern Recognition
High-Performance Rotation Invariant Multiview Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Asymmetric Learning for Cascade Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Design of Cascades of Boosted Ensembles for Face Detection

International Journal of Computer Vision
Facial feature detection using distance vector fields

Pattern Recognition
Locating and extracting the eye in human face images

Pattern Recognition
Fusing Gabor and LBP feature sets for kernel-based face recognition

AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Robust precise eye location by adaboost and SVM techniques

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II

Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion

Computer Vision and Image Understanding
A literature survey on robust and efficient eye localization in real-life scenarios

Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper proposes a novel eye detection method, which can locate the accurate positions of the eyes from frontal face images. The proposed method is robust to pose changes, different facial expressions and illumination variations. Initially, it utilizes image enhancement, Gabor transformation and cluster analysis to extract eye windows. It then localizes the pupil centers by applying two neighborhood operators within the eye windows. Experiments with the color FERET and the LFW (Labeled Face in the Wild) datasets (including a total of 3587 images) are used to evaluate this method. The experimental results demonstrate the consistent robustness and efficiency of the proposed method.