The Community for Technology Leaders
RSS Icon
Issue No.10 - October (2011 vol.33)
pp: 1925-1937
Behrooz Kamgar-Parsi , Naval Research Laboratory, Washington DC
Wallace Lawson , Naval Research Laboratory, Washington DC
Behzad Kamgar-Parsi , Office of Naval Research, Arlington
The interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.
Face recognition, automatic surveillance, human-like classification, morphing facial images, biometrics, open world face recognition.
Behrooz Kamgar-Parsi, Wallace Lawson, Behzad Kamgar-Parsi, "Toward Development of a Face Recognition System for Watchlist Surveillance", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 10, pp. 1925-1937, October 2011, doi:10.1109/TPAMI.2011.68
[1] Y. Bengio, J. Louradour, R. Collobert, and J. Weston, "Curriculum Learning," Proc. Int'l Conf. Machine Learning, pp. 41-48, June 2009.
[2] K. Bowyer, K. Chang, and P. Flynn, "A Survey of Approaches and Challenges in 3D and Multi-Modal 3D+2D Face Recognition," Computer Vision and Image Understanding, vol. 101, no. 1, pp. 1-15, 2006.
[3] P. Buddharaju, I.T. Pavlidis, P. Tsiamyrtzis, and M. Bazakos, "Physiology-Based Face Recognition in the Thermal Infrared Spectrum," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 613-626, Apr. 2007.
[4] R. Chellappa, C.L. Wilson, and S. Sirohey, "Human and Machine Recognition of Faces: A Survey," Proc. IEEE, vol. 83, no. 5, pp. 705-741, May 1995.
[5] T.F. Cootes, G.J. Edwards, and C.J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, June 2001.
[6] S. Fahlman and E. Scott, An Empirical Study of Learning Speed in Back-Propagation Networks. Carnegie Mellon Univ., Computer Science Dept., 1988.
[7] A.S. Georghiades, P.N. Belhumeur, and D.J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, June 2001.
[8] Z. Han, C. Fang, and X. Ding, "Discriminative Prototype Learning in Open Set Face Recognition," Proc. Int'l Conf. Pattern Recognition, 2010.
[9] B. Kamgar-Parsi, B. Kamgar-Parsi, A.K. Jain, and J.E. Dayhoff, "Aircraft Detection: A Case Study in Using Human Similarity Measure," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pp. 1404-1414, Dec. 2001.
[10] B. Kamgar-Parsi, B. Kamgar-Parsi, and A.K. Jain, "Synthetic Eyes," Proc. Fourth Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 412-420, 2003.
[11] B. Kamgar-Parsi and B. Kamgar-Parsi, "Methods of Facial Recognition," US Patent 7,684,595, 2010.
[12] N. Kumar, A.C. Berg, P. Belumeur, and S. Nayar, "Attribute and Simile Classifiers for Face Verification," Proc. IEEE 12th Int'l Conf. Computer Vision, 2009.
[13] D.T. Lee and B.J. Schachter, "Two Algorithms for Constructing a Delaunay Triangulation," Int'l J. Parallel Programming, vol. 9, no. 3, pp. 219-242, June 1980.
[14] F. Li and H. Wechsler, "Open Set Face Recognition Using Transduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1686-1697, Nov. 2005.
[15] R. Lienhart and J. Jochen Maydt, "An Extended Set of Haar-Like Features for Rapid Object Detection," Proc. IEEE Int'l Conf. Image Processing, vol. 1, pp. 900-903, Sept. 2002.
[16] X. Lu and A.K. Jain, "Deformational Modeling for Robust 3D Face Matching," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1346-1357, Aug. 2008.
[17] S. Milborrow and F. Nicolls, "Locating Facial Features with an Extended Active Shape Model," Proc. European Conf. Computer Vision, 2008.
[18] B. Moghaddam, T. Jebara, and A. Pentland, "Bayesian Face Recognition," Pattern Recognition, vol. 33, pp. 1771-1782, 2000.
[19] P.J. Phillips, H. Moon, P.J. Rauss, and S. Rizvi, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
[20] P. Phillips, P. Flynn, T. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and K. Worrek, "Overview of the Face Recognition Grand Challenge," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 947-954, June 2005.
[21] G. Pike, R. Kemp, and N. Brace, "The Psychology of Human Face Recognition," Proc. IEEE Colloquium on Visual Biometrics, 2000.
[22] T. Sim, S. Baker, and M. Bsat, "The CMU Pose, Illumination, and Expression (PIE) Database," Proc. Fifth Int'l Conf. Automatic Face and Gesture Recognition, 2002.
[23] P. Sinha, B. Balas, Y. Ostrovsky, and R. Russel, "Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About," Proc. IEEE, vol. 94, no. 11, pp. 1948-1962, Nov. 2006.
[24] M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[25] W.R. Uttal, T. Baruch, and L. Allen, "The Effect of Combinations of Image Degradations in a Discrimination Task," Perception and Psychophysics, vol. 57, no. 5, pp. 668-681, 1995.
[26] P. Viola and M.J. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[27] H. Wang, S.Z. Li, Y. Wang, and J. Zhang, "Self Quotient Image for Face Recognition," Proc. Int'l Conf. Image Processing, vol. 2, pp. 1397-1400, 2004.
[28] S.-J. Wang and C. Xu, "Biomimetric (Topological) Pattern Recognition—A New Model of Pattern Recognition Theory and Its Application," Proc. Int'l Joint Conf. Neural Networks, vol. 3, pp. 2258-2262, 2003.
[29] H. Wechsler, Reliable Face Recognition Methods: System Design, Implementation and Evaluation. Springer-Verlag, 2006.
[30] J. Wright, A. Young, A. Ganesh, S. Sastry, and Y. Ma, "Robust Face Recognition via SParse Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[31] S. Zhu and J. Zhao, "Facial Feature Points Extraction," Proc. Fifth Int'l Conf. Image and Graphics, 2009.
125 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool