ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Recognition of quantized still face images
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Remote identification of faces: Problems, prospects, and progress
Pattern Recognition Letters
Separability oriented preprocessing for illumination-insensitive face recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Illumination normalization using self-lighting ratios for 3d2d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Modeling and correction of multipath interference in time of flight cameras
Image and Vision Computing
Computer Vision and Image Understanding
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We present a nonstationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in the literature for albedo estimation, but few include the errors in estimates of surface normals and light source direction to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo, for images illuminated by single and multiple light sources. The albedo estimate obtained is subsequently used to generate albedo-free normalized images for recovering the shape of an object. Traditional Shape-from-Shading (SFS) approaches often assume constant/piecewise constant albedo and known light source direction to recover the underlying shape. Using the estimated albedo, the general problem of estimating the shape of an object with varying albedo map and unknown illumination source is reduced to one that can be handled by traditional SFS approaches. Experimental results are provided to show the effectiveness of the approach and its application to illumination-invariant matching and shape recovery. The estimated albedo maps are compared with the ground truth. The maps are used as illumination-invariant signatures for the task of face recognition across illumination variations. The recognition results obtained compare well with the current state-of-the-art approaches. Impressive shape recovery results are obtained using images downloaded from the Web with little control over imaging conditions. The recovered shapes are also used to synthesize novel views under novel illumination conditions.