Optimized pre-processing input plane GPU implementation of an optical face recognition technique using a segmented phase only composite filter

Ouerhani, Yousri; Jridi, Maher; Alfalou, Ayman; Brosseau, Christian

doi:10.1016/j.optcom.2012.09.074

Cited by 42 publications

(17 citation statements)

References 19 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

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“…In this approach, the coordinates of the maximum of the filter output are taken as estimates of the target coordinates in the observed scene. Correlation filters possess a good mathematical basis and they can be implemented by exploiting massive parallelism either in hybrid opto-digital correlators [11,12] or in high-performance hardware such as graphics processing units (GPUs) [13] or field programmable gate arrays (FPGAs) [14] at high rate. Additionally, these filters are capable to reliably recognize a target in highly cluttered and noisy environments [8,15,16].…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Díaz-Ramírez

Cuevas

Kober

et al. 2015

Optics Communications

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a b s t r a c tComposite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

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“…Understanding and optimizing optical processing of information techniques is one of the fundamental issues in communication and information technologies [1][2][3][4][5][6][7][8] . Several methods based on spatial filtering have been used to encrypt images.…”

Section: Introductionmentioning

confidence: 99%

Numerical implementation of the multiple image optical compression and encryption technique

et al. 2015

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In this study, we propose a numerical implementation (using a GPU) of an optimized multiple image compression and encryption technique. We first introduce the double optimization procedure for spectrally multiplexing multiple images. This technique is adapted, for a numerical implementation, from a recently proposed optical setup implementing the Fourier transform (FT) 1 . The new analysis technique is a combination of a spectral fusion based on the properties of FT, a specific spectral filtering, and a quantization of the remaining encoded frequencies using an optimal number of bits. The spectral plane (containing the information to send and/or to store) is decomposed in several independent areas which are assigned according a specific way. In addition, each spectrum is shifted in order to minimize their overlap. The dual purpose of these operations is to optimize the spectral plane allowing us to keep the low-and high-frequency information (compression) and to introduce an additional noise for reconstructing the images (encryption). Our results show that not only can the control of the spectral plane enhance the number of spectra to be merged, but also that a compromise between the compression rate and the quality of the reconstructed images can be tuned. Spectrally multiplexing multiple images defines a first level of encryption. A second level of encryption based on a real key image is used to reinforce encryption. Additionally, we are concerned with optimizing the compression rate by adapting the size of the spectral block to each target image and decreasing the number of bits required to encode each block. This size adaptation is realized by means of the root-mean-square (RMS) time-frequency criterion 2 . We have found that this size adaptation provides a good trade-off between bandwidth of spectral plane and number of reconstructed output images 3 . Secondly, the encryption rate is improved by using a real biometric key and randomly changing the rotation angle of each block before spectral fusion. A numerical implementation of this method using two numerical devices (CPU and GPU) is presented 4 .

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Optimized pre-processing input plane GPU implementation of an optical face recognition technique using a segmented phase only composite filter

Cited by 42 publications

References 19 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

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Numerical implementation of the multiple image optical compression and encryption technique

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