Cascade Face Detection Based on Histograms of Oriented Gradients and Support Vector Machine

Yang, Huachun; Wang, Xu An

doi:10.1109/3pgcic.2015.14

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…In fact, this algorithm encourages several researchers to invest themselves in the search for other solutions for an ideal face detector, and especially in the axis concerning the extraction of features [15] [16]. Indeed, several other types have been proposed as Local Binary Pattern (LBP) [2], SURF [3], HOG [4], and NPD [5]. Although some of the features cited above offer significant discriminatory qualities than the HaarLike features for face detection, they generally increase the computational cost [5].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, how to extract robust and discriminating features that can differentiate a face from a non-face, remains a central and difficult problem. A variety of representations of facial features have been offered in recent years namely HaarLike Features [1] [20], which is the basis of the Viola and Jones algorithm which is the most known in the past two decades, LBP [2], SURF [3], HOG [4] [18], NPD [5], Color and Skin color [6], Eigen face [7], etc.…”

Section: Introductionmentioning

confidence: 99%

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Building Face Detection with Face Divine Proportions

Oualla¹,

Ounachad

Sadiq

2021

Int. J. Onl. Eng.

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<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in an image based on haar-like features to represent the invariant characteristics of a face. The choice of relevant and more representative features is based on the divine proportions of a face. This technique, widely used in the world of beauty, especially in aesthetic medicine, allows the face to be divided into a set of specific regions according to known mathematical measures. Then we used the Adaboost algorithm for the learning phase. All of our work is based on the Viola and Jones algorithm, in particular their innovative technique called Integral Image, which calculates the value of a Haar-Like feature extracted from a face image. In the rest of this article, we will show that our approach is promising and can achieve high detection rates of up to 99%.</span></p>

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Face Detection with Face Divine Proportions

Oualla¹,

Ounachad

Sadiq

2021

Int. J. Onl. Eng.

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“…Authors claim, that LBP (especially the multi block variant) allows to obtain good detection result and reduce the computational complexity. HOG features and SVM (Support Vector Machine) classifier were used in the work [23]. Both issues will be discussed in detail later in this paper.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Multi-scale Face Detection Using HOG Features and SVM Classifier

Drożdż

Kryjak

2016

Image Processing &Amp; Communications

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In this paper an FPGA based embedded vision system for face detection is presented. The sliding detection window, HOG+SVM algorithm and multi-scale image processing were used and extensively described. The applied computation parallelizations allowed to obtain real-time processing of a 1280 × 720 @ 50Hz video stream. The presented module has been verified on the Zybo development board with Zynq SoC device from Xilinx. It can be used in a vast number of vision systems, including diver fatigue monitoring.

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