A novel approach to automatic detection of interest points in multiple facial images

Abstract

The human face includes different colors and forms due to its complexity. Therefore, facial image processing comprises even more problems than image processing of other objects. Interest point detection is one of the important problems in computer vision, which is the key aspect of solving problems such as facial expression analysis, age analysis, sex defining, facial recognition, and three-dimensional face modelling in augmented reality. To accomplish these tasks, facial interest points need automatic definition. A hybrid algorithm was developed to detect automatically interest regions and points in multiple images in the resented study. The study used processed facial images from an authorized image database with a resolution of 1600 x 1200, taken in standardized illumination conditions by using an InSpeck Mega Capturor II optical 3D structured light digitizer and 1000-W halogen lamp. The presented study integrated skin color analysis with the Haar classification method, processing 11 male and 25 female facial images with the developed algorithm. The average accuracy of facial interest point detection was 0.68 mm after testing all images.

Keywords

• Close-range photogrammetry,face recognition and facial interest points,image matching and processing

References

1. Ar, I. (2008). Recognition of Human Behaviours In Video Streams. Seminar report, Gebze Institute of Technology, Dept. of Informatics, Izmit-Turkey.
2. Bansal, A. (2012). Face recognition using PCA and LDA Algorithm. In: Advanced Computing and Communication Technologies (ACCT), Second International Conference. Rohtak- India, 251-254.
3. Bhowmik, M.K., Shil, S. and Saha, P. (2013). Feature Points Extraction of Thermal Face using Harris Interest Point Detection. In: International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), University of Kalyani. West Bengal, India, September 27-28 2013, Procedia Technology 2013; 10: 724 – 730. Bhumika, G. and Zankhana, H. (2011). Face Feature Extraction Techniques: A Survey. In: National Conference on Recent Trends in Engineering & Technology. Anand, Gujarat, India, 13-14 May 2011.
4. Brunelli, R. and Poggio, T. (1993). Face Recognition: Features Versus Templates, IEEE Transactions on PAMI, 15(10), 1042-1052.
5. Çavdaroğlu, G.Ç. (2013). Face Recognition Analysis by Developing Feature Operators In Virtual Reality, MSc Thesis, Yildiz Technical University, Institute of Natural and Applied Sciences, Istanbul-Turkey.
6. Chai, D. and Ngan, K. (1999). Face Segmentation Using Skin Color Map in Videophone Application. Transactions on Circuits and Systems for Video Technology, 9(4): 551-564.
7. Davies, E.R. (2012). Corner and Interest Point Detection, Computer and Machine Vision (Fourt Edition), Chapter 6: 149-184.
8. Delakis, M. and Garcia, C. (2002). Robust Face Detection Based on Convolutional Neural Networks, 2nd Hellenic Conf. on Al., SETN-2002, 11-12 April 2002, Thessaloniki, Gereece, Proceesing: 367-378.

9. Demirel, H. and Anbarjafari, G. (2008). A New Face Recognition System Based On Color Histogram Matching. In: Signal Processing, Communication and Applications Conference, SIU, 22-28 April, Aydin, Turkey: 1–4.
10. Eser, S. (2006). Face Recognition And Tracking By Neural Networks, MS Thesis, Yildiz Technical University, Institute of Natural and Applied Sciences, Istanbul-Turkey.
11. Feris, R.S., Gemmell, J., Toyama, K. and Krüger, V. (2002). Hierarchical Wavelet Networks for Facial Feature Localization. In: Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition. Washington D.C. USA, 21 May, 2002: 118–123.
12. Fröba, B. and Küblbeck, C. (2001). Real-Time Face Detection Using Edge-Orientation Matching. In: Third International Conference, AVBPA , Lecture Notes in Computer Science, Halmstad, Sweden, 6-8 June 2001, 2091: 78-83.
13. Găianua, M. and Onchiş, D.M. (2014). Face and Marker Detection Using Gabor Frames on GPUs. Signal Processing, 96: 90–93.
14. Harmon, L.D. (1977). Automatic recognition of human face profiles, Computer Graphics and Image Processing, 6 (2):135-156.
15. Jazayeri, I. and Fraser, C. (2010). Interest Operators For Feature - Based Matching In Close Range Photogrammetry, The Photogrammetric Record, 25(129): 24–41.
16. Kim, I., Shim, H.J. and Yang, J. (2003). Face detection, Face Detection Project, EE368, Stanford University, Vol. 28.
17. Kondo, T. and Yan, H. (1999). Automatic human face detection and recognition under non-uniform illumination, Pattern Recognition, 32: 1707-1718.
18. Koo, H. and Song, H. (2010). Facial Feature Extraction for Face Modeling Program. Int. J. Circuits, 4(4): 169-176.
19. Kurt, B. (2007). Physiological State Evaluation, MSc Thesis, Black Sea Technical University, Institute of Natural and Applied Sciences, Trabzon-Turkey.
20. Lee, S. and Thalmann, N. (2011). Fast Head Modeling For Animation. Image Vis. Comput., 14 (4): 355-364.
21. Ma, T.C. (2011). 3D Facial Reconstruction System from Skull for Vietnamese. Knowledge and Systems Engineering (KSE) In: Third International Conference. Hano-Vietnam, 14-17 October 2011: 120-127.
22. Poynton, C. A. (1985). Guided Tour Of Color Space. New Foundations for video Technology, In: Proceedings of the SMTPE Advanced Television and Electronic Imaging Conference. San Francisco, USA, Feb. 10-11: 167-180.
23. Reinders, M.J.T., Van Beek, P.J.L., Sankur, B. and Van Der Lubbe, J.C.A. (1995). Facial Feature Localization and Adaptation of A Generic Face Model For Model-Based Coding. Signal Processing: Image Communication, 7: 57–74.
24. Huorong, R., Jianwei, S., Yanhong, H., Xinxin, Y. and Yang, L. (2014). Uniform Local Derivative Patterns and Their Application in Face Recognition. Journal of Signal Processing Systems, 74 (3): 405-416.
25. Rosenfeld, A. (1970). A Connectivity in Digital Pictures. Journal of the ACM (JACM), 17(1): 146–160.
26. Rosenfeld, A. and Kak, A.C. (1976). Digital Picture Processing, New York, Sec 81, Academic Press.
27. Saha, R. and Bhattacharjee, D. (2012). Memory Efficient Human Face Recognition Using Fiducial Points. Int. J. of Adv. Res. in Comput. Sci. and Softw. Eng. 2(1):1-8
28. Samal, A. and Iyengar, P.A. (1992). Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition, 25 (1): 65-77.
29. Savran,N. A., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B. , Sankur, B. and Akarun,L. (2008). Bosphorus Database for 3D Face Analysis. In: The First COST 2101 Biometrics and Identity Management: Workshop on Biometrics and Identity Management (BIOID 2008). Roskilde University, Denmark, 7-9 May, Revised Selected Papers, Lecture Notes in Computer Science, Volume 5372: 47-56.
30. Shin, M., Chang, K. and Tsap, L. (2002). Does Color Space Transformation Make Any Difference On Skin Detection? In: IEEE Workshop on Applications of Computer Vision. Orlando, FL, USA, 3-4 December ISBN 0-7695-1858-3: 275 – 279.
31. Sobottka, K. and Pitas, I. (1996). Extraction of Facial Regions and Features Using Color and Shape Information. In: Proc. of Int. Conf. on Pattern Recognition, 3: 421 – 425.
32. Tian,Y. and Bolle, R.M. (2001). Automatic Neutral Face Detection Using Location And Shape Features, Computer Science Research Report RC 22259, IBM Research.
33. URL 1 (2012). http://www.aforgenet.com/
34. URL 2 (2011). http://www.face-rec.org/databases/
35. URL 3. (2012). http://www.facedetection.com
36. Valentin, D., Abdi, H., O'Toole, A.J. and Cottrell, G.W. (1994). Connectionist models of face processing: a survey, Pattern Recognition, 27: 1209-1230.
37. Valstar, M.F. (2011). The First Facial Expression Recognition and Analysis Challenge. In: Automatic Face & Gesture Recognition and Workshops (FG 2011), IEEE International Conference. Santa Barbara, CA, USA, 21-25 March 2011: 921-926.
38. Viola, P. and Jones, M. (2001). Robust Real-Time Object Detection. In: Second International Workshop On Statistical And Computational Theories Of Vision – Modeling, Learning, Computing And Sampling. Vancouver, Canada, July 13, 2001.
39. Xiaoping, W. (2011). Face Recognition Based on Bionic Pattern. In: Electric Information and Control Engineering (ICEICE), 2011 International Conference, China, 15-17 April 2011: 2187 – 2189.