Robust face detection using haar features with weber local descriptor and local binary pattern

Khammari, Mohammed; Tlili, Yamina; Bencheriet, Chemesse ennehar

doi:10.3233/kes-150313

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…a) javier.diaz@itcelaya.edu.mx b) torresc@tec.mx load and bandwidth for local servers. Nevertheless, the CNN computational complexity is usually higher than traditional algorithms [10,11,12], so the use of graphics processing units and online servers are a frequent choice to accelerate operations in CNNs. Thus, there is an increasing demand for real-time processing solutions for CNNs, mainly within embedded implementations.…”

Section: Introductionmentioning

confidence: 99%

A streaming accelerator of Convolutional Neural Networks for resource-limited applications

Arredondo-Velázquez

Díaz-Carmona

Torres-Huitzil

et al. 2019

IEICE Electron. Express

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Convolutional Neuronal Networks (CNN) implementation on embedded devices is restricted due to the number of layers of some CNN models. In this context, this paper describes a novel architecture based on Layer Operation Chaining (LOC) which uses fewer convolvers than convolution layers. A reutilization of hardware convolvers is promoted through kernel decomposition. Thus, an architectural design with reduced resources utilization is achieved, suitable to be implemented on low-end devices as a solution for portable classification applications. Experimental results show that the proposed design has a competitive processing time and overcomes resource utilization when compared with state-of-the-art related works.

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Section: Introductionmentioning

confidence: 99%