A sparse representation approach to face matching across plastic surgery

Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. In this paper, we propose a novel approach to address the challenges involved in automatic matching of faces across plastic surgery variations. In the proposed formulation, part-wise facial characterization is combined with the recently popular sparse representation approach to address these challenges. The sparse representation approach requires several images per subject in the gallery to function effectively which is often not available in several use-cases, as in the problem we address in this work. The proposed formulation utilizes images from sequestered non-gallery subjects with similar local facial characteristics to fulfill this requirement. Extensive experiments conducted on a recently introduced plastic surgery database [17] consisting of 900 subjects highlight the effectiveness of the proposed approach.

[1]  Bruce A. Draper,et al.  Overview of the Multiple Biometrics Grand Challenge , 2009, ICB.

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[5]  Himanshu S. Bhatt,et al.  Face recognition for newborns: A preliminary study , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[6]  John Q. Owsley,et al.  AMERICAN SOCIETY FOR AESTHETIC PLASTIC SURGERY , 1976 .

[7]  Alice J. O'Toole,et al.  Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Fred Nicolls,et al.  Locating Facial Features with an Extended Active Shape Model , 2008, ECCV.

[9]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[11]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Richa Singh,et al.  Face recognition with disguise and single gallery images , 2009, Image Vis. Comput..

[13]  D. Donoho.  For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[14]  Patrick J. Flynn,et al.  Multidimensional scaling for matching low-resolution facial images , 2010, BTAS.

[15]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[16]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Hermann Ney,et al.  SURF-Face: Face Recognition Under Viewpoint Consistency Constraints , 2009, BMVC.

[19]  Himanshu S. Bhatt,et al.  Plastic Surgery: A New Dimension to Face Recognition , 2010, IEEE Transactions on Information Forensics and Security.

[20]  Rama Chellappa,et al.  Sparsity inspired selection and recognition of iris images , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[21]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..