Eye feature extraction using K-means clustering for low illumination and iris color variety
- 1 December 2010
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
This paper presents an approach for locating eye features in color images based on the unsupervised K-means clustering. Given the assumption that the input is an eye window containing a single eye, the proposed method detects the iris by unsupervised K-means clustering on the feature spaces of compensated red and green color channels. The iris circle is then refined using the gradient information and circular Hough transform. For the sclera detection, the r-g and r-b are utilized as they show the discriminant feature of sciera regardless of light condition and iris color. The sciera is then extended to fit the eyelids by a region growing scheme. Experiments on a collection of eye images extracted from FERET facial database and our self-collected images show a promising performance toward the low illumination and iris color variety.Keywords
This publication has 12 references indexed in Scilit:
- Human eye sclera detection and tracking using a modified time-adaptive self-organizing mapPattern Recognition, 2008
- A robust method for eye features extraction on color imagePattern Recognition Letters, 2005
- A robust method for detecting arbitrarily tilted human faces in color imagesPattern Recognition Letters, 2005
- A Composite Method to Extract Eye ContourLecture Notes in Computer Science, 2005
- Face recognition based on fitting a 3D morphable modelIEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
- Efficient face candidates selector for face detectionPattern Recognition, 2003
- The FERET database and evaluation procedure for face-recognition algorithmsImage and Vision Computing, 1998
- Some defects in finite-difference edge findersIEEE Transactions on Pattern Analysis and Machine Intelligence, 1992
- Use of the Hough transformation to detect lines and curves in picturesCommunications of the ACM, 1972
- LIII. On lines and planes of closest fit to systems of points in spaceJournal of Computers in Education, 1901