Efficient eye detection method based on grey intensity variance and independent components analysis

Detection of facial features such as eye, nose and mouth in the human face images is important for many applications like face identification or recognition systems. Independent components analysis (ICA) is an unsupervised learning method which decorrelates the higher-order statistics in addition to the second-order moments. Recently, it is used as a technique for face recognition. In this study, ICA applied on a patch image is used as a method to extract the eye which is the most salient and stable feature among all the facial features. The variance of grey intensity in the eye region and ICA are combined together to detect rough eye window. The ICA basis images are computed using the FastICA algorithm; that computes independent components by maximising non-Gaussianity of the whitened data distribution using a kurtosis maximisation process. After detecting rough eye window, intensity information is used to localise eye centre point. The proposed method is evaluated on different databases XM2VTS, BioID and FERET and experimental results demonstrate improved performance over the existing methods. In addition, a high detection rate of 93.3% can be achieved on 600 images with glasses. A comparison between the proposed method and the most recent published methods which focus on eye window detection is also reported.

[1]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[2]  Pong C. Yuen,et al.  Multi-cues eye detection on gray intensity image , 2001, Pattern Recognit..

[3]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[4]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[5]  E. Oja,et al.  Independent Component Analysis , 2001 .

[6]  Paola Campadelli,et al.  Fiducial point localization in color images of face foregrounds , 2004, Image Vis. Comput..

[7]  Zhi-Hua Zhou,et al.  Projection functions for eye detection , 2004, Pattern Recognit..

[8]  Se-Young Oh,et al.  Automatic Extraction of Eye and Mouth Fields from a Face Image Using Eigenfeatures and Ensemble Networks , 2004, Applied Intelligence.

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[10]  Jie Yang,et al.  A robust method for eye features extraction on color image , 2005, Pattern Recognit. Lett..

[11]  Ian R. Fasel,et al.  A generative framework for real time object detection and classification , 2005, Comput. Vis. Image Underst..

[12]  Ke Sun,et al.  A Composite Method to Extract Eye Contour , 2005, ACII.

[13]  Pengfei Zhao,et al.  Robust Precise Eye Location by Adaboost and SVM Techniques , 2005, ISNN.

[14]  Su Ruan,et al.  A robust agorithm for eye detection on gray intensity face without spectacles , 2005 .

[15]  Qiong Wang,et al.  Eye Detection in Facial Images With Unconstrained Background , 2006 .

[16]  Jing Xiao,et al.  Meticulously detailed eye region model and its application to analysis of facial images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Yepeng Guan.  Robust Eye Detection from Facial Image based on Multi-cue Facial Information , 2007, 2007 IEEE International Conference on Control and Automation.

[18]  Qiang Ji,et al.  Multi-view face and eye detection using discriminant features , 2007, Comput. Vis. Image Underst..

[19]  Silvio Jamil Ferzoli Guimarães,et al.  Bipartite graph matching for video clip localization , 2007 .

[20]  Reza Safabakhsh,et al.  Human eye sclera detection and tracking using a modified time-adaptive self-organizing map , 2008, Pattern Recognit..

[21]  David Zhang,et al.  Sequential row-column independent component analysis for face recognition , 2009, Neurocomputing.

[22]  俊一 甘利,et al.  A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, Jhon Wiley & Sons, 2001年，504ページ． （根本幾・川勝真喜訳：独立成分分析——信号解析の新しい世界，東京電機大学出版局，2005年，532ページ．） , 2010 .

[23]  Qiang Ji,et al.  In the Eye of the Beholder: A Survey of Models for Eyes and Gaze , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.