Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Position Detection with Face Recognition using Image Processing and Machine Learning Techniques

Novel Aspects of Digital Imaging Applications
© 2011 by IJCA Journal
ISBN: 978-93-80865-47-9
Year of Publication: 2011
G.Suvarna Kumar
P.V.G.D Prasad Reddy
R.Anil Kumar
Sumit Gupta

G.Suvarna Kumar, Prasad P V G D Reddy, R.Anil Kumar and Sumit Gupta. Position Detection with Face Recognitionusing Image Processing and Machine Learning Techniques. IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA) (1):79–86, 2011. Full text available. BibTeX

	author = {G.Suvarna Kumar and P.V.G.D Prasad Reddy and R.Anil Kumar and Sumit Gupta},
	title = {Position Detection with Face Recognitionusing Image Processing and Machine Learning Techniques},
	journal = {IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA)},
	year = {2011},
	number = {1},
	pages = {79--86},
	note = {Full text available}


In this paper, an improved algorithm for detecting the position of a person in controlled environments using the face detection algorithm is proposed. This algorithm ingeniously combines different face detection, occlusion detection algorithms, EMD for facial recognition and SVM classifier. A class room environment with thirty students is used along with some constraints such as position of the camera being fixed in a way that covers all the students, the quasi-static student’s position and the class environment with the fixed lighting conditions. For every class, a set of 6 attributes are derived and updated in a database. The image is given as an input to the face detection algorithm to detect some of the faces. Some faces are not detected because of occlusion, so an occlusion detection technique is implemented to detect all the occluded faces. Using the EMD based face recognition techniques, missing positions are correlated with individuals assuming a quasi-static setup. Experiments have been conducted in different classroom settings and accuracies of more than 96% have been obtained. In this paper lib SVM is used.


  • M.Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, Mar. 1991, pp. 71-86.
  • The Face Detection Homepage,
  • Yohei Kawaguchi, Tetsuo Shoji, Weijane Lin, Koh Kakusho,Michihiko Minoh, “Face Recognition based Attendance System” .
  • C. Lawrence Zitnick, Takeo Kanade, “A Cooperative Algorithm for Stereo Matching and Occlusion Detection” CMU-RI-TR-99-35.
  • H. C. Vijaya Lakshmi, D. Patil Kulakarni “Segmentation algorithm for multiple face detection in color images with skin tone regions using color spaces and edge detection techniques,” International journal of computer theory and engineering 1793-8201,2010.
  • Rafael. C. Gonzalez, Richard. E. Woods. “Digital Image Processing. Pearson Education, Second Edition,” India, 2002.
  • Mikael Nilsson, J¨orgen Nordberg, and Ingvar Claesson, “Face detection using local SMQT features and split up SNOW classifier” 20(12):1222– 1239, November 2006.
  • O. Lahdenoja, M. Laiho, and A. Paasio, “Reducing the feature vector length in local binary pattern based face recognition,” in IEEE International Conference on Image Processing (ICIP), September 2005, vol. 2, pp. 914–917.
  • B. Froba and A. Ernst, “Face detection with the modified census transform,” in Sixth IEEE International Conference on AutomaticFace and Gesture Recognition, May 2004, pp. 91–96.
  • M. Nilsson, M. Dahl, and I. Claesson, “The successive mean quantization transform,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2005, vol. 4, pp. 429–432.
  • M.-H. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no. 1, pp. 34–58, 2002.
  • H. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” in In Procedings of Computer Vision and Pattern Recognition, June 1996, pp. 203–208.
  • H. Schneiderman and T. Kanade, “Probabilistic modeling of local appearance and spatial relationships for object recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’98), July 1998, pp. 45– 51.
  • P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in In Proceedings of the 2001IEEE Computer Society Conference on Computer Vision andPattern Recognition (CVPR), 2001, vol. 1, pp. 511–518.
  • D. Roth, M. Yang, and N. Ahuja, “A snow-based face detector,” in In Advances in Neural Information Processing Systems 12 (NIPS 12), pp. 855–861, MIT Press 2000.
  • E. Osuna, R. Freund, and F. Girosi, “Training support vector machines:an application to face detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’97), 1997, pp. 193–199.
  • Math open Reference, available at
  • Math Warehouse, available at
  • Niranjan, M. “Support vector machines: a tutorial overview and critical appraisal” in Applied Statistical Pattern Recognition (Ref. No. 1999/063), IEE Colloquium on, 20 Apr 1999, ref number 1999/063.
  • Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas, “The Earth Mover's Distance as a Metric for Image Retrieval”
  • Sylvain Boltz, Frank Nielsen, Stefano Soatto, “Earth Movers Distance On SuperPixels”, Ecole Polytechnique, France,UCLA Vision Lab