Proceedings ArticleDOI
Illumination modeling and normalization for face recognition
Haitao Wang,Stan Z. Li,Yangsheng Wang,Weiwei Zhang +3 more
- pp 104-111
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TLDR
This work shows that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights, and presents a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image.Abstract:
We present a general framework for face modeling under varying lighting conditions. First, we show that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights. The lighting of any face image can be represented as a point in this subspace. Second, we show that the extreme rays, i.e. the boundary of an illumination cone, cover the entire light sphere. Therefore, a relatively sparsely sampled face images can be used to build a face model instead of calculating each extremely illuminated face image. Third, we present a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image. Experiments are presented.read more
Citations
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Journal ArticleDOI
Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics
Lei Zhang,Dimitris Samaras +1 more
TL;DR: Two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information are proposed.
Lighting Normalization Algorithms for Face Verification
TL;DR: The effect of various photometric normalization algorithms on the performance of a system based on local feature extraction and generative models (Gaussian Mixture Models) and two state-of-the-art approaches: the Self Quotient Image and an anisotropic diffusion based normalization.
Proceedings ArticleDOI
An Active Illumination and Appearance (AIA) Model for Face Alignment
TL;DR: This paper proposes an approach that integrates the face identity and illumination models under the widely used active appearance model framework as an extension to the texture model in order to obtain illumination-invariant face localization.
Proceedings ArticleDOI
Robust Face Alignment for Illumination and Pose Invariant Face Recognition
TL;DR: A robust face alignment approach is developed by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set and the experimental results show that the combined pose alignment and illuminationnormalization methods increase the recognition rates considerably for all feature-spaces.
Journal ArticleDOI
Adaptive active appearance model with incremental learning
Jaewon Sung,Daijin Kim +1 more
TL;DR: An adaptive AAM is proposed that updates the appearance basis vectors with the current face image by the incremental principal component analysis (PCA) and is superior to the conventional AAM in terms of the occurrence rate of fitting error and the fitting accuracy.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
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Neural network-based face detection
TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Journal ArticleDOI
Illumination for computer generated pictures
TL;DR: Human visual perception and the fundamental laws of optics are considered in the development of a shading rule that provides better quality and increased realism in generated images.
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