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Proceedings Article

Face detection on distorted images using perceptual quality-aware features

[+] Author Affiliations
Suriya Gunasekar, Joydeep Ghosh, Alan C. Bovik

The Univ. of Texas at Austin (United States)

Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90141E (February 25, 2014); doi:10.1117/12.2037343
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From Conference Volume 9014

  • Human Vision and Electronic Imaging XIX
  • Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder
  • San Francisco, California, United States | February 02, 2014

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/. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Suriya Gunasekar ; Joydeep Ghosh and Alan C. Bovik
" Face detection on distorted images using perceptual quality-aware features ", Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90141E (February 25, 2014); doi:10.1117/12.2037343; http://dx.doi.org/10.1117/12.2037343


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