GPU Accelerated Face Recognition System with Enhanced Local Ternary Patterns Using OpenCL

Vinith, B; Akhila, M.; Naik, Narmada; Rathna, G. N.

doi:10.1109/dicta.2015.7371263

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…In [38] results based on comparison between Intel core i3 CPU @ 2.67GHz clock and AMD 6500 GPU showed that GPU processor parallel implementation resulted approximately in 50 times faster processing than the CPU. Similarly, in [46] experimental results performed using AMD Radeon HD 7650 GPU and Intel core i3 CPU for 4096x4096 image showed that GPU achieved 374x speed up compared to the CPU. In [49], AMD 6500 GPU was compared against Dual-core CPU and achieved 30x speed up for 1024 x 1024 image size.…”

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 76%

“…Therefore, GPUs performance surpasses CPUs due to their large number of cores and due to the fact that these algorithms require fine-grain parallelism. Papers [35], [38], [46], and [49] have compared the performance of both accelerators and all concluded that a GPU outperformed multicores-CPU in terms of accuracy and processing time.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…This technique demonstrated higher recognition results than the original LTP method on the same three datasets. Similarly, in [46] an improvement to the accuracy is achieved by introducing Enhanced Local Ternary Pattern (ELTP), which has also surpassed the original LBP and LTP techniques. Likewise, a fast parallelized face recognition based on LBP that obtained an accuracy of approximately 85% was suggested in [38].…”

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 99%

“…However, the use of advanced image processing techniques improved the recognition accuracy to 99% on a dataset of around 760 faces and improved the detection accuracy to 95%. With regard to LBP-based hardware accelerators, a GPUbased parallelized approach of LBP for face recognition using AMD's Radeon HD 7650 GPU processor was described in [46] using OpenCL programming language. The parallel implementation consists to allocate to each GPU core of the processor a sub-block of the input image, which is adequate since this avoids using inter-block communications which is very time consuming.…”

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 99%

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Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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Real-time face recognition has been of great interest in the last decade due to its wide and variant critical applications which include biometrics, security in public places, and identification in login systems. This has encouraged researchers to design fast and accurate embedded and portable systems that are capable to detect and recognize a large number of faces at almost video frame rate. Due to the increasing volume of reference faces, traditional general purpose computing engines such as the ones based on Intel's Pentium processors have shown not to be adequate and various dedicated hardware accelerators based on either Graphical Processing Unit (GPU), Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), or even multi-core Central Processing Units (CPU) have emerged. Earlier published review papers on face detection/recognition have discussed face detection and face recognition algorithms enhancement that improve the detection accuracy. Nevertheless, none of them has reviewed the hardware accelerators used for this application. Accordingly, this paper aims to provide a comprehensive review of the most recent face recognition algorithms and associated embedded hardware systems targeting real-time performance. A detailed comparison between neural network and non-neural network-based algorithms in terms of accuracy and processing time is provided. Discussions on their suitability to be implemented into parallel hardware architectures such as Single Instruction Multiple Thread (SIMT) or Single Instruction Multiple Data (SIMD) is also discussed.

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Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 76%

Section: Discussion and Recommendationsmentioning

confidence: 99%

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 99%

Section: Face Detection/recognition Algorithms Targeting Real-time An...mentioning

confidence: 99%

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Hardware Accelerators for Real-Time Face Recognition: A Survey

2022

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“…Nowadays, GPU has been widely used in various fields, especially in computer vision and image processing [4]. GPU contains thousands of cores to process parallel workloads efficiently.…”

Section: Introductionmentioning

confidence: 99%

GPU accelerated Foreign Object Debris Detection on Airfield Pavement with visual saliency algorithm

Gong

Cao

2017

J. Phys.: Conf. Ser.

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Abstract. We present a GPU-based implementation of visual saliency algorithm to detect foreign object debris(FOD) on airfield pavement with effectiveness and efficiency. Visual saliency algorithm is introduced in FOD detection for the first time. We improve the image signature algorithm to target at FOD detection in complex background of pavement. First, we make pooling operations in obtaining saliency map to improve recall rate. Then, connected component analysis is applied to filter candidate regions in saliency map to get the final targets in original image. Besides, we map the algorithm to GPU-based kernels and data structures. The parallel version of the algorithm is able to get the results with 23.5 times speedup. Experimental results elucidate that the proposed method is effective to detect FOD real-time.

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A Fast Face Recognition Method Based on Fractal Coding

Tang

Leng

et al. 2017

SIViP

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GPU Accelerated Face Recognition System with Enhanced Local Ternary Patterns Using OpenCL

Cited by 4 publications

References 3 publications

Hardware Accelerators for Real-Time Face Recognition: A Survey

Hardware Accelerators for Real-Time Face Recognition: A Survey

GPU accelerated Foreign Object Debris Detection on Airfield Pavement with visual saliency algorithm

A Fast Face Recognition Method Based on Fractal Coding

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