Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A signal-processing framework for inverse rendering
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Dynamic Range Reduction Inspired by Photoreceptor Physiology
IEEE Transactions on Visualization and Computer Graphics
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Robust multipose face detection in images
IEEE Transactions on Circuits and Systems for Video Technology
Perception-Based lighting adjustment of image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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For robust face detection, lighting is considered as one of the greatest challenges. The three-step face detection framework provides a practical method for real-time face detection. In this framework, the last step can employ computation extensive method to remove the false alarm and usually some de-lighting methods are done. It is complex to model the lighting variance precisely. The usually used simplified lighting model fails under non-uniform lighting conditions for the reason that it cannot account for the cast shadow, shading, and highlight, which are the main variances caused by non-uniform lighting. According to the adaptation capacity of the human vision system, we propose a perception based mapping method (PMM) to balance the influence of non-uniform lighting. Experimental results indicate that with PMM as the lighting-filter the false positives caused by lighting variance can be removed more accurately in the face detection tasks. PMM shows its outstanding performance especially under the extreme lighting conditions.