Impact of resolution and image quality on video face analysis

Low-resolution face analysis suffers more significantly from quality degradations than high-resolution analysis. In this work, we will investigate how several face analysis steps are influenced by low image quality and how this relates to the low resolution. In the first step, a simulation of different effects on image quality, namely low resolution, compression artifacts, motion blur and noise is performed and the impact on face detection, registration and recognition is analyzed. Depending on the situation, it becomes obvious that the low resolution is sometimes a minor degrading effect, outmatched by a single one or a combination of the further effects. When addressing real-world face recognition from surveillance data, the combination of the challenging effects is the biggest problem because typical counter measures are individual to one single effect.

[1]  Jürgen Beyerer,et al.  Face Retrieval on Large-Scale Video Data , 2015, 2015 12th Conference on Computer and Robot Vision.

[2]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Christian Herrmann,et al.  Extending a local matching face recognition approach to low-resolution video , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[6]  Likun Huang,et al.  Face recognition based on image sets , 2014 .

[7]  Yi-Ching Liaw,et al.  Artifact reduction of JPEG coded images using mean-removed classified vector quantization , 2002, Signal Process..

[8]  Osamu Yamaguchi,et al.  Face Recognition Using Multi-viewpoint Patterns for Robot Vision , 2003, ISRR.

[9]  Rama Chellappa,et al.  Motion Deblurring: Algorithms and Systems , 2014 .

[10]  Jürgen Beyerer,et al.  Low-resolution video face recognition with face normalization and feature adaptation , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[11]  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.

[12]  Q. M. Jonathan Wu,et al.  Low-resolution face recognition: a review , 2013, The Visual Computer.

[13]  Rainer Stiefelhagen,et al.  Multi-pose Face Recognition for Person Retrieval in Camera Networks , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[14]  Xiaogang Wang,et al.  Hallucinating face by eigentransformation , 2005, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Tal Hassner,et al.  Face recognition in unconstrained videos with matched background similarity , 2011, CVPR 2011.

[16]  Andrew Zisserman,et al.  A Compact and Discriminative Face Track Descriptor , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Jean-Philippe Thiran,et al.  Towards robust cascaded regression for face alignment in the wild , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[18]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..