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International Journal of Latest Research in Science and Technology

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ENSEMBLE OF FACE/EYE DETECTORS FOR ACCURATE AUTOMATIC FACE DETECTION

Research Paper Open Access

International Journal of Latest Research in Science and Technology Vol.4 Issue 3, pp 8-18,Year 2015

ENSEMBLE OF FACE/EYE DETECTORS FOR ACCURATE AUTOMATIC FACE DETECTION

Loris Nanni,Alessandra Lumini,Sheryl Brahnam

Correspondence should be addressed to :

Received : 16 April 2015; Accepted : 26 May 2015 ; Published : 30 June 2015

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Article No. 10518
Abstract

In this work we propose a simple yet effective face detector that combines several face/eye detectors that possess different characteristics. Specifically, we report an extensive study for combining face/eye detectors that results in a final system we call FED that combines three face detectors that extract regions of candidate faces from an image with two approaches for eye detection: the enhanced Pictorial Structure (PS) model for coarse eye localization and a new approach proposed here (called PEC) that provides precise eye localization. PEC is an ensemble that utilizes three texture descriptors: multi-resolution local ternary patterns, local phase quantization descriptors, and patterns of oriented edge magnitudes. The extracted features are coupled with support vector machines trained on eye and non-eye samples to perform classification. The proposed framework for face detection could be considered an ad hoc integration of existing methods (the three face detectors and the PS coarse eye detector) that is combined with the proposed novel ensemble for precise eye localization (PEC). The aim of this approach is to maximize performance (not computation time). The quality of the proposed system is validated on three datasets (the well-known BioID and FERET datasets as well as a self-collected dataset). To the best of our knowledge, our system is one of the first fully automatic face detection approaches to obtain an accuracy of almost 100% on the BioID dataset (the most important benchmark dataset for frontal face detection) and 99.1% using the same dataset with only 12 false positives. A MATLAB version of our complete system for face detection can be downloaded from https://www.dei.unipd.it/node/2357.

Key Words   
Eye detection; face detection; texture descriptors; local phase quantization; feature combination; s

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References
  1. Zeng Z., Pantic M., Roisman G.I., and Huang T.S., “A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39-58, Jan. 2009.
  2. L. Jin, Q. S. Liu, and H. Q. Lu, “Face detection using one-class based support vectors,” in Proc. 6th IEEE Int. Conf. Autom. Face Gesture Recog., 2004, pp. 457–462.
  3. Zhang C. and Zhang Z., "A Survey of Recent Advances in Face Detection", Microsoft Research Technical Report, MSR-TR-2010-66, Jun. 2010.
  4. Hansen D. and Ji Q., “In the eye of the beholder: a survey of models for eyes and gaze”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 478–500, 2010.
  5. Campadelli P., Lanzarotti R., Lipori G., Precise Eye and Mouth Localization. IJPRAI 23(3): 359-377 (2009).
  6. Min J., Bowyer K., Flynn P., “Eye Perturbation Approach for Robust Recognition of Inaccurately Aligned Faces”, Hilton Rye Town, NY, USA, July 20-22, 2005, 544-554.
  7. Kroon B., Maas S., Boughorbel S., Hanjalic A., Eye localization in low and standard definition content with application to face matching. Computer Vision and Image Understanding, 113(8):921-933, 2009.
  8. Tan X., and Triggs B., “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” Analysis and Modelling of Faces and Gestures, vol. LNCS 4778, pp. 168-182, 2007.
  9. Ojansivu V., and Heikkila J., “Blur insensitive texture classification using local phase quantization”, in ICISP, 2008.
  10. Sun and Z. Ma, Robust and Efficient Eye Location and Its State Detection, Advances in computation and intelligence, Lecture Notes in Computer Science, 2009, Volume 5821/2009, pages 318-326.
  11. Wu J., Zhou Z.-H., Efficient face candidates selector for face detection, Pattern Recognition 36 (2003) 1175 – 1186.
  12. Nilsson M., Nordberg J., Claesson I., “Face detection using local SMQT features and split up SNOW classifier”, in IEEE International conference on Acoustics, Speech, and signal processing (ICASSP), 2007, vol 2, pp. 589-592.
  13. Battiato S., Farinella G.M., Guarnera M., Messina G., Ravì D., “Red-Eyes Removal through Cluster Based Linear Discriminant Analysis”, Proceedings of IEEE ICIP 2010 - International Conference on Image Processing, Hong Kong, September 2010.
  14. Safonov, I.V.: Automatic red-eye detection. In: GraphiCon., International conference on the Computer Graphics and Vision, Moscow, Russia (2007).
  15. Jesorsky O., Kirchberg K., Frischholz R., Robust face detection using the Hausdorff distance, in proc. Int. Conf. on Audio- and Video-Based Biometric Person Authentication, pp. 90-95, 2001.
  16. Cristinacce and T.F. Cootes. Feature detection and tracking with constrained local models. Proc. the British Machine Vision Conf., 3:929-938, 2006.
  17. Kim, S. Chung, S. Jung, D. Oh, J. Kim, and S. Cho. Multi-scale gabor feature based eye localization. Proc. of World Academy of Science, Engineering and Technology, 21:483-487, 2007.
  18. Tang, Z. Ou, T. Su, H. Sun and P. Zhao. Robust Precise Eye Location by AdaBoost and SVM Techniques. Proc. Int'l Symposium on Neural Networks, pages 93-98, 2005.
  19. Xiaoyang Tan; Fengyi Song; Zhi-Hua Zhou; Songcan Chen, "Enhanced Pictorial Structures for precise eye localization under incontrolled conditions," Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on , vol., no., pp.1621,1628, 20-25 June 2009
  20. Loris Nanni, Alessandra Lumini: Combining Face and Eye Detectors in a High- Performance Face-Detection System. IEEE MultiMedia 19(4): 20-27 (2012)
  21. Ngoc-Son Vu, Hannah M. Dee and Alice Caplier "Face Recognition using the POEM descriptor", Pattern Recognition Volume 45 Issue 7, July, 2012 Pages 2478-2488.
  22. Paul viola and Michael J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", CVPR 2001.
  23. Loris Nanni, Alessandra Lumini, Mauro Migliardi: Learning based Skin Classification. International Journal of Automated Identification Technology, In Press.
  24. Dong Yi, Zhen Lei, Stan Z. Li. “A Robust Eye Localization Method for Low Quality Face Images”, International Joint Conference on Biometrics, 2011.
  25. Riopka and T. Boult. The eyes have it. In Proceedings of ACM SIGMM Multimedia Biometrics Methods and Applications Workshop, pages 9–16, 2003.
  26. Tan, Enhanced pictorial structures for precise eye localization under uncontrolled conditions, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1621–1628
  27. Jung, L.C. Jiao, T. Sun, Illumination invariant eye detection in facial Images based on the Retinex theory, in: Proceeding of IscIDE, 2011, pp. 175–183
  28. Cheolkon Jung, Tian Sun, Licheng Jiao, Eye detection under varying illumination using the retinex theory, Neurocomputing, Volume 113, 3 August 2013, Pages 130-137.
  29. Jiayu Gu, Chengjun Liu, Feature local binary patterns with application to eye detection, Neurocomputing, Volume 113, 3 August 2013, Pages 138-152
  30. Zhou, X. Geng, Projection functions for eye detection, Pattern Recognition, 37 (5) (2004) 1049–1056
  31. Felzenszwalb and P. Huttenlocher. Pictorial structures for object recognition. IJCV, 61(1):1573–1405, 2005
  32. Fawcett, Tom (2004); ROC Graphs: Notes and Practical Considerations for Researchers, Pattern Recognition Letters, 27(8):882-891.
  33. Yang, J. Huang, P. Yang, and D. Metaxas. Eye localization through mul-tiscale sparse dictionaries. In Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, pages 514–518, 2011.
  34. N. Belhumeur, D. W. Jacobs, D. J. Kriegman, N. Kumar, "Localizing Parts of Faces Using a Consensus of Exemplars", Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  35. Charles Brubaker, Jianxin Wu, Jie Sun, Matthew D. Mullin, James M. Rehg, On the Design of Cascades of Boosted Ensembles for Face Detection, International Journal of Computer Vision, May 2008, Volume 77, Issue 1-3, pp 65-86
  36. Fasel, I., Fortenberry, B., Movellan, J.: A generative framework for real time object detection and classification. Computer Vision Image Understand. 98, 182-210, 2005
  37. Huang, H. Ai, Y. Li, S. Lao, High-performance rotation invariant multiview face detection, Pattern Analysis and Machine Intelligence, IEEE Transactions on 29 (4), 671-686
  38. Verschae, J. Ruiz-del-Solar, M. Correa, A unified learning framework for object detection and classification using nested cascades of boosted classifiers, Machine Vision and Applications, Vol 19, pp 85-103, 2008
  39. Garcia, M. Delakis, "Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1408-1423, November, 2004.

 

To cite this article

Loris Nanni,Alessandra Lumini,Sheryl Brahnam , " Ensemble Of Face/eye Detectors For Accurate Automatic Face Detection ", International Journal of Latest Research in Science and Technology . Vol. 4, Issue 3, pp 8-18 , 2015

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