18 January 2010 Face detection assisted auto exposure: supporting evidence from a psychophysical study
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Abstract
Face detection has been implemented in many digital still cameras and camera phones with the promise of enhancing existing camera functions (e.g. auto exposure) and adding new features to cameras (e.g. blink detection). In this study we examined the use of face detection algorithms in assisting auto exposure (AE). The set of 706 images, used in this study, was captured using Canon Digital Single Lens Reflex cameras and subsequently processed with an image processing pipeline. A psychophysical study was performed to obtain optimal exposure along with the upper and lower bounds of exposure for all 706 images. Three methods of marking faces were utilized: manual marking, face detection algorithm A (FD-A), and face detection algorithm B (FD-B). The manual marking method found 751 faces in 426 images, which served as the ground-truth for face regions of interest. The remaining images do not have any faces or the faces are too small to be considered detectable. The two face detection algorithms are different in resource requirements and in performance. FD-A uses less memory and gate counts compared to FD-B, but FD-B detects more faces and has less false positives. A face detection assisted auto exposure algorithm was developed and tested against the evaluation results from the psychophysical study. The AE test results showed noticeable improvement when faces were detected and used in auto exposure. However, the presence of false positives would negatively impact the added benefit.
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Elaine W. Jin, Elaine W. Jin, Sheng Lin, Sheng Lin, Dhandapani Dharumalingam, Dhandapani Dharumalingam, } "Face detection assisted auto exposure: supporting evidence from a psychophysical study", Proc. SPIE 7537, Digital Photography VI, 75370K (18 January 2010); doi: 10.1117/12.839094; https://doi.org/10.1117/12.839094
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