Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

Wong, Yongkang, Chen, Shaokang, Mau, Sandra, Sanderson, Conrad and Lovell, Brian C. (2011). Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, United States, (74-81). 20-25 June 2011. doi:10.1109/CVPRW.2011.5981881


Author Wong, Yongkang
Chen, Shaokang
Mau, Sandra
Sanderson, Conrad
Lovell, Brian C.
Title of paper Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition
Conference name 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Conference location Colorado Springs, CO, United States
Conference dates 20-25 June 2011
Proceedings title 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)   Check publisher's open access policy
Journal name IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/CVPRW.2011.5981881
ISBN 9781457705298
ISSN 2160-7508
Start page 74
End page 81
Total pages 8
Collection year 2012
Language eng
Abstract/Summary In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ subset of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 21 Dec 2011, 15:24:02 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering