Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
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
Symmetric Shape-from-Shading Using Self-ratio Image
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
Face recognition under variable lighting using harmonic image exemplars
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Illumination normalization for color face images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Selection of wavelet subbands using genetic algorithm for face recognition
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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We propose a simplified and practical computational technique for estimating directional lighting in uncalibrated images of faces in frontal pose. We show that this inverse problem can be solved using constrained least-squares and class-specific priors on shape and reflectance. For simplicity, the principal illuminant is modeled as a mixture of Lambertian and ambient components. By using a generic 3D face shape and an average 2D albedo we can efficiently compute the directional lighting with surprising accuracy (in real-time and with or without shadows). We then use our lighting direction estimate in a forward rendering step to “relight” arbitrarily-lit input faces to a canonical (diffuse) form as needed for illumination-invariant face verification. Experimental results with the Yale Face Database B as well as real access-control datasets illustrate the advantages over existing pre-processing techniques such as a linear ramp (facet) model commonly used for lighting normalization.