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Pose-Robust Recognition of Low-Resolution Face Images
Dec. 2013 (vol. 35 no. 12)
pp. 3037-3049
Soma Biswas, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Gaurav Aggarwal, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Patrick J. Flynn, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Kevin W. Bowyer, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Face images captured by surveillance cameras usually have poor resolution in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a completely automatic, novel approach for matching surveillance quality facial images to high-resolution images in frontal pose, which are often available during enrollment. The proposed approach uses multidimensional scaling to simultaneously transform the features from the poor quality probe images and the high-quality gallery images in such a manner that the distances between them approximate the distances had the probe images been captured in the same conditions as the gallery images. Tensor analysis is used for facial landmark localization in the low-resolution uncontrolled probe images for computing the features. Thorough evaluation on the Multi-PIE dataset and comparisons with state-of-the-art super-resolution and classifier-based approaches are performed to illustrate the usefulness of the proposed approach. Experiments on surveillance imagery further signify the applicability of the framework. We also show the usefulness of the proposed approach for the application of tracking and recognition in surveillance videos.
Index Terms:
video surveillance,image classification,image matching,image recognition,object tracking,pose estimation,tensors,surveillance video recognition,pose-robust recognition,low-resolution face images,surveillance cameras,face matching algorithms,surveillance quality facial image matching,high-resolution images,frontal pose images,multidimensional scaling,poor quality probe images,high-quality gallery images,tensor analysis,facial landmark localization,low-resolution uncontrolled probe images,MultiPIE dataset,super-resolution,classifier-based approaches,surveillance imagery,surveillance video tracking,Facial recognition,Iterative methods,Resolution,Cameras,Surveillance,iterative majorization,Face recognition,low-resolution matching,multidimensional scaling
Citation:
Soma Biswas, Gaurav Aggarwal, Patrick J. Flynn, Kevin W. Bowyer, "Pose-Robust Recognition of Low-Resolution Face Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 3037-3049, Dec. 2013, doi:10.1109/TPAMI.2013.68
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