Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
A Two-Level Generative Model for Cloth Representation and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled statistical face reconstruction
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Effect of facial feature points selection on 3d face shape reconstruction using regularization
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
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In this paper, we develop a novel framework for robust recovery of three-dimensional (3-D) surfaces of faces from single images. The underlying principle is shape from recognition, i.e., the idea that pre-recognizing face parts can constrain the space of possible solutions to the image irradiance equation, thus allowing robust recovery of the 3-D structure of a specific part. Parts of faces like nose, lips and eyes are recognized and localized using robust expansion matching filter templates under varying pose and illumination. Specialized backpropagation based neural networks are then employed to recover the 3-D shape of particular face parts. Representation using principal components allows to efficiently encode classes of objects such as nose, lips, etc. The specialized networks are designed and trained to map the principal component coefficients of the part images to another set of principal component coefficients that represent the corresponding 3-D surface shapes. To achieve robustness to viewing conditions, the network is trained with a wide range of illumination and viewing directions. A method for merging recovered 3-D surface regions by minimizing the sum squared error in overlapping areas is also derived. Quantitative analysis of the reconstruction of the surface parts in varying illumination and pose show relatively small errors, indicating that the method is robust and accurate. Several examples showing recovery of the complete face also illustrate the efficacy of the approach