Pixel features series fusion for precise facial components localisation in probabilistic framework

REFERENCES

• Wang P, Ji Q. Multi-view face and eye detection using discriminate features. Comput. Vis. Imag. Underst., 2007, 105, 55–62.
   Google Scholar
• Niu Z, Shan S, Yan S, Chen X, Gao W. 2D cascaded AdaBoost for eye localization, Proc. 18th Int. Conf. on Pattern Recognition: ICPR’06, Hong Kong, China, August 2006, IEEE Computer Society, Vol. 2, pp. 1216–1219.
   Google Scholar
• Viola P, Jones M. Robust real time object detection. Int. J. Comput. Vis., 2004, 572, 137–154.
   Google Scholar
• Campadell P, Lanzarott R, Lipor G. Precise eye and mouth localization. Int. J. Patt. Recogn. Artif. Intell., 2009, 23, 359–377.
   Web of Science ®Google Scholar
• Tang X, Ou Z, Su T, Sun H, Zhao P. Robust precise eye location by AdaBoost and SVM techniques. Lect. Notes Comput. Sci., 2005, 3497, 93–98.
   Google Scholar
• Everingham M, Zisserman A. Regression and classification approaches to eye localization in face images, Proc. 7th IEEE Int. Conf. on Automatic face and gesture recognition: FG’06, Southampton, UK, April2006, IEEE Computer Society, pp. 441–448.
   Google Scholar
• Fasel I, Fortenberry B, Movellan J. A generative framework for real time object detection and classification. Comput. Vis. Imag. Underst., 2005, 98, 182–210.
   Web of Science ®Google Scholar
• Ma Y, Ding X, Wang Z, Wang N. Robust precise eye location under probabilistic framework, Proc. 6th IEEE Int. Conf. Automatic face and gesture recognition: FG’04, Seoul, Korea, May2004, IEEE Computer Society, pp. 339–344.
   Google Scholar
• Keren D, Osadchy M, Gotsman C. Antifaces: a novel, fast method for image detection, IEEE Trans. Patt. Anal. Mach. Intell., 2001, 23, 747–761.
   Web of Science ®Google Scholar
• Osadchy M, Keren D. Incorporating the Boltzmann prior in object detection using SVM, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR’06, New York, USA, June2006, IEEE Computer Society, Vol. 2, pp. 2095–2101.
   Google Scholar
• Amit Y, Geman D, Fan X. A coarse-to-fine strategy for multiclass shape detection. IEEE Trans. Patt. Anal. Mach. Intell., 2004, 26, 1606–1621.
   PubMed Web of Science ®Google Scholar
• Kumatani K, Stiefelhagen R. Mouth region localization method based on Gaussian mixture model. Lect. Notes Comput. Sci., 2006, 4153, 115–124.
   Google Scholar
• Hsu RL, Abdel-Mottaleb M, Jain AK. Face detection in color images. IEEE Trans. Patt. Anal. Mach. Intell., 2002, 24, 696–706.
   Web of Science ®Google Scholar
• Yuille AL, Hallinan P, Cohen D. Feature extraction from faces using deformable templates. Int. J. Comput. Vis., 1992, 8, 99–111.
   Web of Science ®Google Scholar
• Mohammad HM, Mohamed AM, Ansari AN. Improved active shape model for facial feature extraction in color images. J. Multimedia, 2006, 1, 21–28.
   Google Scholar
• Cristinacce D, Cootes TF. Feature detection and tracking with constrained local models, Poc. British Machine Vision Conf.: BMVC’06, Edinburgh, UK, September 2006, BMVA, Vol. 3, pp. 929–938.
   Google Scholar
• Milborrow S, Nicolls F. Locating facial features with an extended active shape model, Proc. 10th European Conference on Computer Vision: ECCV’08, Marseille, France, October 2008, Part IV, pp. 504–513.
   Google Scholar
• Kim S, Chung S, Jung S, Oh D, Kim J, Cho S. Multi-scale Gabor feature based eye localization, Proc. World Academy of Science, Engineering and Technology, Prague, Czech Republic, October2007, INRIA Grenoble – Rhône-Alpes, Vol. 22, pp. 483–487.
   Google Scholar
• Maia J, Gomes F, Souza O. Automatic eye localization in color images, Proc. Brazilian Symp. on Computer graphics and image processing: SIBGRAPI 2007, Minas Gerais, Brazil, October2007, Brazilian Computer Society, pp. 195–204.
   Google Scholar
• Wencong Z, Hong C, Peng Y, Bin L, Zhenquan Z. Precise eye localization with Adaboost and fast radial symmetry, Proc. Int. Conf. on Computational intelligence and security: CIS’06, Guangzhou, China, November2006, IEEE, Vol. 1, pp. 725–730.
   Google Scholar
• Lienhart R, Maydt J. An extended set of Haar-like features for rapid object detection, Proc. IEEE Int. Conference on Image processing: ICIP’02, Rochester, NY, USA, September2002, IEEE Computer Society, Vol. 1, pp. 900–903.
   Google Scholar
• Castrillón M, Déniz O, Hernández D, Lorenzo J. A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework. Mach. Vis. Appl., 2010, 22, 481–494.
   Web of Science ®Google Scholar
• Wang P, Ji Q. Multi-view face and eye detection using discriminant features. Comput. Vis. Imag. Underst., 2007, 105, 55–62.
   Google Scholar
• Jin L, Yuan X, Satoh S, Li J, Xia L. A hybrid classifier for precise and robust eye detection, Proc. IEEE Int. Conf. on Pattern recognition: ICPR’06, Hong Kong, China, IEEE Computer Society, August2006, Vol. 4, pp. 731–735.
   Google Scholar
• Li X, Wang L, Sung E. AdaBoost with SVM-based component classifiers. Eng. Appl. Artif. Intell., 2008, 21, 785–795.
   Web of Science ®Google Scholar
• Castrillón MS, Déniz OS, Hernández MT, Guerra CA. ENCARA2: real-time detection of multiple faces at different resolutions in video streams. J. Vis. Commun. Image Represent., 2007, 18, 130–140.
   Web of Science ®Google Scholar
• Lin K, Huang J, Chen J, Zhou C. Real-time eye detection in video streams, Proc. 4th Int. Conf. on Natural computation ICNC’08, Jinan, China, IEEE Computer Society, August2008, Vol. 6, pp. 193–197.
   Google Scholar
• Gorodnichy DO, Roth G. Nouse ‘use your nose as a mouse’ perceptual vision technology for hands-free games and interfaces. Image Vis. Comput., 2004, 22, 931–942.
   Web of Science ®Google Scholar
• Ren X, Song J, Ying H, Zhu Y, Qiu X. Robust nose detection and tracking using GentleBoost and improved Lucas–Kanade optical flow algorithms. Lect. Notes Comput. Sci.: Adv. Intell. Comput. Theor. Appl., 2007, 4681, 1240–1246.
   Google Scholar
• Liang L, Liu X, Pi X, Zhao Y, Nefian AV. Speaker independent audio-visual continuous speech recognition, Proc. Int. Conf. on Multimedia and Expo: ICME’02, Lausanne, Switzerland, August2002, IEEE Computer Society, Vol. 2, pp. 25–28.
   Google Scholar
• Ding L, Martinez AM. Features versus context: an approach for precise and detailed detection and delineation of faces and facial features. IEEE Trans. Patt. Anal. Mach. Intell., 2010, 32, 2022–2038.
   PubMed Web of Science ®Google Scholar
• Wang J, Kou Z, Ji L. Facial feature point extraction by partial least square regression [online], 2002 [accessed 4 April 2010]. Available at: <http://citeseerx.ist.psu.edu/viewdoc/download?doi = 10·1·1. 12·4306&rep = rep 1&type = pdf>
   Google Scholar
• Wan Y, Du J, Li K. A novel feature extraction approach to face recognition based on partial least squares regression. Lect. Notes Comput. Sci., 2006, 4113, 1078–1084.
   Google Scholar
• Mayer C, Wimmer M, Radig B. Adjusted pixel features for robust facial component classification. Image Vis. Comput., 2010, 28, 762–771.
   Web of Science ®Google Scholar
• Zhu YM, Nortmann CA. Pixel-feature hybrid fusion for PET/CT images. J. Digit Imag., 2010, 24, 54–57.
   Google Scholar
• Nissenboim I, Keren D, Werman M. Applying two-pixel features to face detection, Proc. IEEE Int. Conf. on Signal image technology and Internet based systems: SITIS’08, Bali, Indonesia, November2008, IEEE Computer Society, pp. 65–69.
   Google Scholar
• Ahmed R, Karmakar GC, Dooley LS. Texture as pixel feature for video object segmentation. Electron. Lett., 2008, 44, 1126–1127.
   Web of Science ®Google Scholar
• Poynton CA. A Technical Introduction to Digital Video, 1996 (John Wiley & Sons, New York).
   Google Scholar
• Lin C. Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network. Patt. Recogn. Lett., 2007, 28, 2190–2200.
   Web of Science ®Google Scholar
• Zhou Z, Geng X. Projection functions for eye detection. Patt. Recogn., 2004, 37, 1049–1056.
   Web of Science ®Google Scholar
• Gross R, Matthews I, Cohn J, Kanade T, Baker S. ‘Guide to the CMU multi-PIE database’, Technical report, Carnegie Mellon University, Pittsburgh, PA, USA, 2007.
   Google Scholar
• Martinez M, Benavente R. ‘The AR face database’, CVC Tech. Report #24, 1998.
   Google Scholar
• Paalanen P, Kamarainen J, Ilonen J, Kälviäinen H. Feature representation and discrimination based on Gaussian mixture model probability densities — practices and algorithms. Patt. Recogn., 2006, 39, 1346–1358.
   Web of Science ®Google Scholar
• Dinov D. Expectation Maximization and Mixture Modeling Tutorial [online], California Digital Library, Statistics Online Computational Resource [accessed 9 December 2008]. Available at: <http://repositories.cdlib.org/socr/EM_MM>
   Google Scholar
• Alpaydin E. Introduction to Machine Learning (Adaptive Computation and Machine Learning), 2010, 2nd edition (The MIT Press, Cambridge, MA).
   Google Scholar
• Conaire CO, O’Connor NE, Smeaton AF. Detector adaptation by maximising agreement between independent data sources, Proc. IEEE Conf. on Computer vision and pattern recognition: CVPR’07, Minneapolis, MN, USA, June2007, IEEE Computer Society, pp. 1–6.
   Google Scholar