Journal ArticleDOI
Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation
TLDR
This work proposes a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion, and demonstrates how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination.Abstract:
Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.read more
Citations
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Journal ArticleDOI
Sparse Representation for Computer Vision and Pattern Recognition
TL;DR: This review paper highlights a few representative examples of how the interaction between sparse signal representation and computer vision can enrich both fields, and raises a number of open questions for further study.
IEEE transactions on pattern analysis and machine intelligence
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Book ChapterDOI
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
TL;DR: Yadira et al. as mentioned in this paper proposed a simple convolutional neural network to regress the 3D shape of a complete face from a single 2D image, which can reconstruct full facial geometry along with semantic meaning.
Journal ArticleDOI
Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
Weihong Deng,Jiani Hu,Jun Guo +2 more
TL;DR: Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages.
Journal ArticleDOI
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification
TL;DR: The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.
References
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Journal ArticleDOI
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