25 February 2014 Face detection on distorted images using perceptual quality-aware features
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Abstract
We quantify the degradation in performance of a popular and effective face detector when human–perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality–aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non–face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.
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Suriya Gunasekar, Suriya Gunasekar, Joydeep Ghosh, Joydeep Ghosh, Alan C. Bovik, Alan C. Bovik, } "Face detection on distorted images using perceptual quality-aware features", Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90141E (25 February 2014); doi: 10.1117/12.2037343; https://doi.org/10.1117/12.2037343
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