Variant of Normalized Pixel Difference Face Detection Algorithm

IJCSEC Front Page

Due to massive growth of video information and their usage in surveillance applications, a more sophisticated and an intelligent examination systems development is far more critical and exceptionally important. Peoples are one of most common and very specific objects in video which were tracked with the aid of face detection algorithms. Although face detection algorithms are inherently complex due to the complex environment (illumination, pose, age, color coding, occlusion etc.), we have seen the development of some intelligent and accurate face detection algorithms to achieve the intended goal of face localization. In this paper, we have made an attempt to study and modify Normalized Pixel Difference (NPD) based face detection algorithm and explore their merits and suitability by considering different situations. We have alsodescribed detectiontechnique with some application domain along with different challenges in this field.

Keywords: Face detection, normalized pixel difference, deep quad tree, AdaBoost.


  1. Paul Viola, Michael J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision 57(2), 2004.
  2. Sanjay Kr. Singh1, D. S. Chauhan, Mayank Vatsa, Richa Singh A Robust Skin Color Based Face Detection Algorithm Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234 2003.
  3. Phung, S. L., Chai, D. K., & Bouzerdoum, A. Skin colour based face detection. Proceedings of 7th Australian and New Zealand Intelligent Information Systems Conference. Australia. IEEE. pp. 0. 2001.
  4. RaginiChoudhuryVerma, CordeliaSchmid, and KrystianMikolajczyk, ―Face Detection and Tracking in a Video by Propagating Detection Probabilities, ieee transactions on pattern analysis and machine intelligence, vol. 25, no. 10, october 2003.
  5. H.A. Rowley, S. Baluja, and T. Kanade, ―Neural Networks Based Face Detection, IEEE Trans. Pattern Analysis an Machine Intelligence, vol. 20, no. 1, pp. 22-38, Jan. 1998.
  6. K.K. Sung and T. Poggio, ―Example-Based Learning for View-Based Human Face Detection, IEEE Trans. Pattern Analysis andMachine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
  7. J.-C. Terrillon, M. Shirazi, H. Fukamachi, and S. Akamatsu,―Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images, Proc. Int‗l Conf. Automatic Face and Gesture Recognition, pp. 54-61, 2000.
  8. D. Decarlo and D. Metaxas, ―Deformable Model Based Face Shape and Motion Estimation, Proc. Int‗l Conf. Face and Gesture Recognition, 1996.
  9. G. Hager and K. Toyama, ―X Vision: A Portable Substrate for Real- Time Vision Applications,‖ Computer Vision and Image Understanding, vol. 69, no. 1, pp. 23-37, 1998.
  10. J. Yang and A. Waibel, ―Tracking Human Faces in Real Time, Technical Report CMU-CS-95-210, School of Computer Science, Carnegie Mellon Univ., Pittsburgh, Pa., 1995.
  11. J. P. Kapur, ―Face Detection in Color Images,‖ University of Washington Department of Electrical Engineering, 1997. [Online]. Available: [Accessed 10 December 2013].
  12. Cahi, D. and Ngan, K. N., ―Face Segmentation Using Skin-Color Map in Videophone Applications,‖ IEEE Transaction on Circuit and Systems for Video Technology, Vol. 9, pp. 551-564. 1999.
  13. Christopher M. Bishop, Pattern Recognition and Machine Learning, first edition, Springer 2006.
  14. Sanjay Kr. Singh, A Robust Skin Color Based Face Detection Algorithm, Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234 2003.
  15. Crowley, J. L. and Coutaz, J., ―Vision for Man Machine Interaction,‖ Robotics and Autonomous Systems, Vol. 19, pp. 347-358, 1997.
  16. Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain."Face detection in color images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5 :696-706, 2002.
  17. Mohamed Theo Gevers, ―Robust Segmentation and Tracking of Colored Objects in Video‖,CSVT, IEEE, pp. 178-181 Jul 2004.
  18. A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell. 23 (6) (2001) 643–660.
  19. MayankChauha and MukeshSakle―Study & Analysis of Different Face Detection Techniques.‖ International Journal of Computer Science and Information Technologies, Vol. 5 (2), 2014, 1615-1618.
  20. G. Yang and T. S. Huang, ―Human Face Detection in Complex Background,‖ Pattern Recognition, vol. 27,no.1, p. 53-63, 994.
  21. T.K. Leung, M.C. Burl, and P. Perona, ―Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching, Proc. Fifth IEEE Int‘l Conf. Computer Vision, pp. 637-644, 1995.
  22. K.C. Yow and R. Cipolla, ―Feature-Based Human Face Detection, Image and Vision Computing, vol.15, no.9, pp 713-735, 1997.
  23. J. Yang and A. Waibel, ―A Real-Time Face Tracker,Proc.Third Workshop Applications of Computer Vision, pp. 142-147,1996.