The Experts below are selected from a list of 32226 Experts worldwide ranked by ideXlab platform
Ryan Kastner - One of the best experts on this subject based on the ideXlab platform.
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FCCM - Design and Implementation of an FPGA-Based Real-Time Face Recognition System
2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines, 2011Co-Authors: Janarbek Matai, Ali Irturk, Ryan KastnerAbstract:Face Recognition Systems play a vital role in many applications including surveillance, biometrics and security. In this work, we present a {\textit complete} real-time Face Recognition System consisting of a Face detection, a Recognition and a down sampling module using an FPGA. Our System provides an end-to-end solution for Face Recognition, it receives video input from a camera, detects the locations of the Face(s) using the Viola-Jones algorithm, subsequently recognizes each Face using the EigenFace algorithm, and outputs the results to a display. Experimental results show that our complete Face Recognition System operates at 45 frames per second on a Virtex-5 FPGA.
Hossei Mobahi - One of the best experts on this subject based on the ideXlab platform.
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toward a practical Face Recognition System robust alignment and illumination by sparse representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Co-Authors: Andrew Wagne, J Wrigh, Arvind Ganesh, Ziha Zhou, Hossei MobahiAbstract: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.
Gwojong Yu - One of the best experts on this subject based on the ideXlab platform.
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a new lda based Face Recognition System which can solve the small sample size problem
Pattern Recognition, 2000Co-Authors: Lifen Chen, Hongyuan Mark Liao, Mingtat Ko, Gwojong YuAbstract:A new LDA-based Face Recognition System is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a Face Recognition System signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Andrew Wagne - One of the best experts on this subject based on the ideXlab platform.
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toward a practical Face Recognition System robust alignment and illumination by sparse representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Co-Authors: Andrew Wagne, J Wrigh, Arvind Ganesh, Ziha Zhou, Hossei MobahiAbstract: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.
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towards a practical Face Recognition System robust registration and illumination by sparse representation
Computer Vision and Pattern Recognition, 2009Co-Authors: Andrew Wagne, J Wrigh, Arvind Ganesh, Ziha ZhouAbstract:Most contemporary Face Recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical Face Recognition System that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test Face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public Face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete Face Recognition System, including a projector-based training acquisition System, in order to evaluate how our algorithms work under practical testing conditions. We show that our System can efficiently and effectively recognize Faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.
Janarbek Matai - One of the best experts on this subject based on the ideXlab platform.
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FCCM - Design and Implementation of an FPGA-Based Real-Time Face Recognition System
2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines, 2011Co-Authors: Janarbek Matai, Ali Irturk, Ryan KastnerAbstract:Face Recognition Systems play a vital role in many applications including surveillance, biometrics and security. In this work, we present a {\textit complete} real-time Face Recognition System consisting of a Face detection, a Recognition and a down sampling module using an FPGA. Our System provides an end-to-end solution for Face Recognition, it receives video input from a camera, detects the locations of the Face(s) using the Viola-Jones algorithm, subsequently recognizes each Face using the EigenFace algorithm, and outputs the results to a display. Experimental results show that our complete Face Recognition System operates at 45 frames per second on a Virtex-5 FPGA.