The Experts below are selected from a list of 61644 Experts worldwide ranked by ideXlab platform
Michael J Jones - One of the best experts on this subject based on the ideXlab platform.
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Robust Real-Time Face Detection
International Journal of Computer Vision, 2004Co-Authors: Paul Viola, Mj Michael J. Jones, Michael J JonesAbstract:This paper describes a Face Detection framework that is capable of processing images extremely rapidly while achieving high Detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo-rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows back-ground regions of the image to be quickly discarded while spending more computation on promising Face-like regions. A set of experiments in the domain of Face Detection is presented. The system yields Face Detection perfor-mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, Face Detection proceeds at 15 frames per second.
Xiaoou Tang - One of the best experts on this subject based on the ideXlab platform.
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wider Face a Face Detection benchmark
Computer Vision and Pattern Recognition, 2016Co-Authors: Shuo Yang, Chen Change Loy, Ping Luo, Xiaoou TangAbstract:Face Detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of Face Detection benchmark datasets. We show that there is a gap between current Face Detection performance and the real world requirements. To facilitate future Face Detection research, we introduce the WIDER Face dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and Face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER Face dataset is an effective training source for Face Detection. We benchmark several representative Detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated.
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WIDER Face: A Face Detection benchmark
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016Co-Authors: Shuo Yang, Chen Change Loy, Ping Luo, Xiaoou TangAbstract:Face Detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of Face Detection benchmark datasets. We show that there is a gap between current Face Detection performance and the real world requirements. To facilitate future Face Detection research, we introduce the WIDER Face dataset, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and Face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER Face dataset is an effective training source for Face Detection. We benchmark several representative Detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated. Dataset can be downloaded at: mmlab.ie.cuhk.edu.hk/projects/WIDERFace
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CVPR - WIDER Face: A Face Detection Benchmark
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Co-Authors: Shuo Yang, Chen Change Loy, Ping Luo, Xiaoou TangAbstract:Face Detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of Face Detection benchmark datasets. We show that there is a gap between current Face Detection performance and the real world requirements. To facilitate future Face Detection research, we introduce the WIDER Face dataset1, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and Face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER Face dataset is an effective training source for Face Detection. We benchmark several representative Detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated.
C.w. Tao - One of the best experts on this subject based on the ideXlab platform.
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SMC - Face Detection Using EigenFace and Neural Network
2006 IEEE International Conference on Systems Man and Cybernetics, 2006Co-Authors: Chung-chih Tsai, W.c. Cheng, Jin-shiuh Taur, C.w. TaoAbstract:Face recognition is a popular research topic in the biometric identification area. Face Detection and localization is the most important pre-processing module of a Face recognition system. The purpose of Face Detection is to search and localize the positions of Faces in an image in the varied background. In this paper, we propose a Face Detection system which combines the eigenFace algorithm with the neural network. The candidate Face region is examined to see if it is a Face image block by analyzing the geometrical distribution of the edges in the region. In our experiments, the proposed Face Detection system has a high Face Detection rate and less false Detections.
Theocharis Theocharides - One of the best experts on this subject based on the ideXlab platform.
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Embedded hardware Face Detection for digital surveillance systems
2006Co-Authors: V. Narayanan, Theocharis TheocharidesAbstract:The digital surveillance market is expected to reach USD $7 billion by 2008, at an average annual growth rate of 55%. More importantly however, this growth is enhanced by the increasing needs of security and control systems used in heavily trafficked areas such as airports, transportation hubs and public buildings. Human Face Detection in real-time video is one of the most important applications in the field. Performed mostly in software so far, it has not been applied in real-time video frame rates. With today's technology however, we are capable of designing hardware platforms to perform Face Detection in real-time video and allow for deployment of multiple cameras to be used for Detection. Multi-camera Face Detection offers significant cost cutting solutions for deploying a surveillance mechanism consisting of multiple cameras and a single high-speed platform. Such a platform, however, must provide reliable data transmission from each camera to the base station; as such, an error correction mechanism which achieves excellent block performance (capable of detecting and correcting large chunks of data) and operates at high-throughput is necessary. This thesis presents a framework for an embedded Face Detection platform for digital surveillance systems, including reliable video transmission. Firstly, the design of a Low-Density Parity Check (LDPC) Decoder is presented. The decoder architecture is suitable for providing a reliable and high-bandwidth communications channel between multiple cameras and the base station. Next, this thesis focuses on explorations for hardware architectures for Face Detection algorithms. One of the most popular Face Detection algorithms is the AdaBoost classification technique, offering significant advantages in terms of speed and accuracy over other algorithms. Given the advantages of AdaBoost, this thesis presents the design of an architecture which performs Face Detection using AdaBoost, achieving high frame rates in conditions where the corresponding software approach slows significantly. The AdaBoost technique however demands a large number of hardware resources, hence an alternative method, Artificial Neural Network (ANN) based Face Detection is investigated. The proposed architecture designed to implement ANN based Face Detection, processes 24 frames per second and is presented along with an FPGA prototype implementation. ANNs are also used in several other applications other than Face Detection, such as Face recognition which usually follows Face Detection. As such, the design of an ANN architecture using Networks-On-Chip is presented next. The presented architecture can be used to perform Face Detection using ANNs, as well as several other ANN applications. All presented architectures achieve high frame rates, and maintain Detection accuracy comparable to software implementations.
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embedded hardware Face Detection
International Conference on VLSI Design, 2004Co-Authors: Theocharis Theocharides, Narayanan Vijaykrishnan, G M Link, M J IrwinAbstract:Face Detection is the first step towards Face recognition and is a vital task in surveillance and security applications. Current software implementations of Face Detection algorithms lack the computational ability to support Detection in real time video streams. Consequently, this work focuses on the design of special-purpose hardware for performing rotation invariant Face Detection. The synthesized design using 160 nm technology is found to operate at 409.5 kHz providing a throughput of 424 frames per second and consumes 7 Watts of power. The synthesized design provided 75% accuracy in detecting Faces from a set of 55 images that is competitive with existing software implementations that provide around 80-85% accuracy.
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VLSI Design - Embedded hardware Face Detection
17th International Conference on VLSI Design. Proceedings., 2004Co-Authors: Theocharis Theocharides, Narayanan Vijaykrishnan, G M Link, M J IrwinAbstract:Face Detection is the first step towards Face recognition and is a vital task in surveillance and security applications. Current software implementations of Face Detection algorithms lack the computational ability to support Detection in real time video streams. Consequently, this work focuses on the design of special-purpose hardware for performing rotation invariant Face Detection. The synthesized design using 160 nm technology is found to operate at 409.5 kHz providing a throughput of 424 frames per second and consumes 7 Watts of power. The synthesized design provided 75% accuracy in detecting Faces from a set of 55 images that is competitive with existing software implementations that provide around 80-85% accuracy.
Paul Viola - One of the best experts on this subject based on the ideXlab platform.
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Robust Real-Time Face Detection
International Journal of Computer Vision, 2004Co-Authors: Paul Viola, Mj Michael J. Jones, Michael J JonesAbstract:This paper describes a Face Detection framework that is capable of processing images extremely rapidly while achieving high Detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo-rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows back-ground regions of the image to be quickly discarded while spending more computation on promising Face-like regions. A set of experiments in the domain of Face Detection is presented. The system yields Face Detection perfor-mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, Face Detection proceeds at 15 frames per second.
