HW/SW Co-Design of the HOG algorithm on a Xilinx Zynq SoC

Rettkowski, Jens; Boutros, Andrew; Göhringer, Diana

doi:10.1016/j.jpdc.2017.05.005

Cited by 47 publications

(28 citation statements)

References 6 publications

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“…Also, it takes into account the particularity of the face as a natural form to identify and use a reduced number of parameters. In addition, these techniques describe the local features through pixel orientations, histograms [13,26], geometric properties, and correlation planes [3,33,41].…”

Section: Local Appearance-based Techniquesmentioning

confidence: 99%

“…In order to facilitate the design of a further face recognition system and make it more robust, pre-processing steps are performed. Many techniques are used to detect and locate the human face image, for example, Viola-Jones detector [24,25], histogram of oriented gradient (HOG) [13,26], and principal component analysis (PCA) [27,28]. Also, the face detection step can be used for video and image classification, object detection [29], region-of-interest detection [30], and so on.…”

mentioning

confidence: 99%

“…The process of this technique done by sharing the whole face image into cells (small region or area); a histogram of pixel edge direction or direction gradients is generated of each cell; and, finally, the histograms of the whole cells are combined to extract the feature of the face image. The feature vector computation by the HOG descriptor proceeds as follows [10,13,26,45]: firstly, divide the local image into regions called cells, and then calculate the amplitude of the first-order gradients of each cell in both the horizontal and vertical direction. The most common method is to apply a 1D mask, [-1 0 1].…”

mentioning

confidence: 99%

“…LQP is a generalization of local pattern features and is intrinsically robust to illumination conditions. The LQP features use the disk layout to sample descriptor proceeds as follows [10,13,26,45]: firstly, divide the local image into regions called cells, and then calculate the amplitude of the first-order gradients of each cell in both the horizontal and vertical direction. The most common method is to apply a 1D mask, [-1 0 1].…”

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confidence: 99%

See 3 more Smart Citations

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

388

146

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Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

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Section: Local Appearance-based Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

388

146

View full text Add to dashboard Cite

show abstract

“…In the domain of low power, high performance embedded systems, the usage of dynamic reconfigurable hardware has been proven as highly efficient for signal and data processing in several application domains [SCH15] and [RBG17]. Reconfigurable hardware is indeed not that energy-and computationally-efficient as application specific integrated circuits (ASIC), but has the advantage, that adaptivity during runtime in soft-and hardware can be exploited [GRI15].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptive Processor Architectures

Fricke

Mahmood

Hoffmann

et al. 2020

Technologien Für Die Intelligente Automation

Self Cite

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The ongoing megatrends in industry and academia like the Internet of Things (IoT), the Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS) present the developers of modern computer architectures with various challenges. A novel class of processors which provide more data throughput with a simultaneously tremendously reduced energy consumption are required as a backbone for these "Things". Additionally, the requirements of CPS like realtime, reliability, dependability, safety, and security are gaining in importance in these applications. This paper shows a brief overview of novel processor architectures providing high flexibility to adapt during design-and runtime to changing requirements of the application and the internal and external system status.© Der/die Herausgeber bzw. der/die Autor(en) 2020 J. Jasperneite, V. Lohweg Kommunikation und Bildverarbeitung in der Automation, (Hrsg.), Technologien für die intelligente Automation 12, https://doi.

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Implementation of a face recognition system as experimental practices in an artificial intelligence and pattern recognition course

Fernández

Vicente

Martínez-Rach

2020

Comp Applic In Engineering

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In this paper, we present the methodology developed in a course of Artificial Intelligence and Pattern Recognition. The methodology is focused on building a face recognition system. For this purpose, we developed an own face recognition database named “Medfaces”. This database contains controlled and wild images for the same subjects, and it also contains infrared images and facial expression labels. This methodology has been applied for teaching telecommunications engineering course at Miguel Hernández University of Elche (Spain). We also present the results of our teaching experience in terms of student satisfaction.

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HW/SW Co-Design of the HOG algorithm on a Xilinx Zynq SoC

Cited by 47 publications

References 6 publications

Face Recognition Systems: A Survey

Face Recognition Systems: A Survey

Domain Adaptive Processor Architectures

Implementation of a face recognition system as experimental practices in an artificial intelligence and pattern recognition course

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