Fast face recognition approach using a graphical processing unit &amp;#x201C;GPU&amp;#x201D;

Ouerhani, Yousri; Jridi, Maher; Alfalou, Ayman

doi:10.1109/ist.2010.5548545

Cited by 22 publications

(20 citation statements)

References 9 publications

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“…Another software implementation of smart cameras is described in [15], where a real-time object tracking system is developed to transmit only high-level information. In [16,17], a GPU implementation of a face recognition scheme using spectral correlation has been developed and a speedup of 4× was obtained compared to implementation based on a generic processor. On the other hand, the hardware implementation is often based on FPGA.…”

Section: Related Workmentioning

confidence: 99%

SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform

Jridi

Chapel

Dorez

et al. 2018

JLPEA

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This paper presents an algorithm/architecture and Hardware/Software co-designs for implementing a digital edge computing layer on a Zynq platform in the context of the Internet of Multimedia Things (IoMT). Traditional cloud computing is no longer suitable for applications that require image processing due to cloud latency and privacy concerns. With edge computing, data are processed, analyzed, and encrypted very close to the device, which enable the ability to secure data and act rapidly on connected things. The proposed edge computing system is composed of a reconfigurable module to simultaneously compress and encrypt multiple images, along with wireless image transmission and display functionalities. A lightweight implementation of the proposed design is obtained by approximate computing of the discrete cosine transform (DCT) and by using a simple chaotic generator which greatly enhances the encryption efficiency. The deployed solution includes four configurations based on HW/SW partitioning in order to handle the compromise between execution time, area, and energy consumption. It was found with the experimental setup that by moving more components to hardware execution, a timing speedup of more than nine times could be achieved with a negligible amount of energy consumption. The power efficiency was then enhanced by a ratio of 7.7 times.

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Section: Related Workmentioning

confidence: 99%

SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform

Jridi

Chapel

Dorez

et al. 2018

JLPEA

Self Cite

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“…Y. Ouerhani, M. Jridi, and A. Alfalou (2010) identified performance results of correlation method for face recognition on GPU. The speedup they have achieved is maximum 4X [5]. CYN Dwith and Dr. Rathna G.N.…”

Section: B Related Workmentioning

confidence: 99%

Super-fast parallel eigenface implementation on GPU for face recognition

Devani¹,

Nikam²,

Meshram³

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting every training image onto it to extract weight vectors. This approach works well with small set of images. As number of images to train increases, time taken for generating eigenspace and weight vectors also increases rapidly and it will not be feasible to recognize face in big data or perform real time video analysis. In this paper, we propose a super-fast parallel solution which harnesses the power of GPU and utilizes benefits of the thousands of cores to compute accurate match in fraction of second. We have implemented Parallel Eigenface, the first complete super-fast Parallel Eigenface implementation for face recognition, using CUDA on NVIDIA K20 GPU. Focus of the research has been to gain maximum performance by implementing highly optimized kernels for complete approach and utilizing available fastest library functions. We have used dataset of different size for training and noted very high increase in speedup. We are able to achieve highest 460X speed up for weight vectors generation of 1000 training images .We also get 73X speedup for overall training process on the same dataset. Speedup tends to increase with respect to training data, proving the scalability of solution. Results prove that our parallel implementation is best fit for various video analytics applications and real time face recognition. It also shows strong promise for excessive use of GPUs in face recognition systems.

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“…On the one hand, this procedure crucially relies on the compatibility of optical correlation methods with this experimental approach. On the other hand, it has been reported in several studies [26][27][28][29][30][31][32][33] that the correlation method employed has good detection and identification performances. However, due to practical reasons, only a numerical implementation of the correlator was considered.…”

Section: Optical Correlationmentioning

confidence: 99%

“…The advantage of using correlation relies on the possibility to create multiple-reference composed filters allowing us to increase robustness and speed [31]. Recently, electronic implementation of correlation filters on field programmable gate array [32] and graphics processing unit [33] give the possibility to perform numerically correlation in real time with good performance and less difficulties than optically.…”

Section: Optical Correlationmentioning

confidence: 99%

Exploring underwater target detection by imaging polarimetry and correlation techniques

et al. 2013

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Underwater target detection is investigated by combining active polarization imaging and optical correlation-based approaches. Experiments were conducted in a glass tank filled with tap water with diluted milk or seawater and containing targets of arbitrary polarimetric responses. We found that target estimation obtained by imaging with two orthogonal polarization states always improves detection performances when correlation is used as detection criterion. This experimentally study illustrates the potential of polarization imaging for underwater target detection and opens interesting perspectives for the development of underwater imaging systems.

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Fast face recognition approach using a graphical processing unit “GPU”

Cited by 22 publications

References 9 publications

SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform

SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform

Super-fast parallel eigenface implementation on GPU for face recognition

Exploring underwater target detection by imaging polarimetry and correlation techniques

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