Face recognition performance with superresolution.

With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.

[1]  Hyeonjoon Moon,et al.  The FERET verification testing protocol for face recognition algorithms , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[2]  Sridha Sridharan,et al.  Evaluation of image resolution and super-resolution on face recognition performance , 2012, J. Vis. Commun. Image Represent..

[3]  Pablo H. Hennings-Yeomans,et al.  Simultaneous super-resolution and feature extraction for recognition of low-resolution faces , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Luuk J. Spreeuwers,et al.  The Effect of Image Resolution on the Performance of a Face Recognition System , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[5]  Xiaoming Liu,et al.  Multi-Frame Super-Resolution for Face Recognition , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[6]  Sharath Pankanti,et al.  Error analysis of pattern recognition systems - the subsets bootstrap , 2004, Comput. Vis. Image Underst..

[7]  Ming-Chao Chiang,et al.  Super-Resolution via Image Warping , 2002 .

[8]  Hua Huang,et al.  Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features , 2011, IEEE Transactions on Neural Networks.

[9]  P. Jonathon Phillips,et al.  Facial Recognition Vendor Test 2000: Evaluation Report , 2001 .

[10]  Takeo Kanade,et al.  Hallucinating faces , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[11]  Ronald G Driggers,et al.  Superresolution image reconstruction from a sequence of aliased imagery. , 2006, Applied optics.

[12]  S. Susan Young,et al.  Super-resolution image reconstruction from a sequence of aliased imagery , 2005, SPIE Defense + Commercial Sensing.

[13]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[14]  Yücel Altunbasak,et al.  Eigenface-domain super-resolution for face recognition , 2003, IEEE Trans. Image Process..

[15]  Alice J. O'Toole,et al.  A video database of moving faces and people , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.