A Novel Eye Localization Method With Rotation Invariance

This paper presents a novel learning method for precise eye localization, a challenge to be solved in order to improve the performance of face processing algorithms. Few existing approaches can directly detect and localize eyes with arbitrary angels in predicted eye regions, face images, and origina...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2014-01, Vol.23 (1), p.226-239
Main Authors: Ren, Yan, Wang, Shuang, Hou, Biao, Ma, Jingjing
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Biometry - methods
Exact sciences and technology
Eye - anatomy & histology
Eye localization
Eye Movements - physiology
Face
Feature extraction
Heating
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Information, signal and communications theory
Learning systems
Male
Ocular Physiological Phenomena
Pattern Recognition, Automated - methods
Rotation
rotation invariance
Sensitivity and Specificity
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Testing
Training
Vectors
Young Adult
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel learning method for precise eye localization, a challenge to be solved in order to improve the performance of face processing algorithms. Few existing approaches can directly detect and localize eyes with arbitrary angels in predicted eye regions, face images, and original portraits at the same time. To preserve rotation invariant property throughout the entire eye localization framework, a codebook of invariant local features is proposed for the representation of eye patterns. A heat map is then generated by integrating a 2-class sparse representation classifier with a pyramid-like detecting and locating strategy to fulfill the task of discriminative classification and precise localization. Furthermore, a series of prior information is adopted to improve the localization precision and accuracy. Experimental results on three different databases show that our method is capable of effectively locating eyes in arbitrary rotation situations (360° in plane).
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2013.2287614