25 February 2014 Face detection on distorted images using perceptual quality-aware features
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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/.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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