Face detection techniques: a review

Kumar, Ashu; Kaur, Amandeep; Kumar, Munish

doi:10.1007/s10462-018-9650-2

Cited by 256 publications

(70 citation statements)

References 27 publications

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“…Given a color (R,G,B) where R, G, B = intensity of red, green and blue, this can be converted to color (r,g,b) where imply the proportion of red, green and blue in the original color [8]. The transformation of Color images in RGB color space is transformed into HSV color space using [5,6,7].…”

Section: Skin Detectionmentioning

confidence: 99%

A Robust and Efficient System to Detect Human Faces Based on Facial Features

Ali

El‐Hafeez

Mohany

2019

AJRCoS

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Face detection is considered as a one of the most important issues in the identification and authentication systems which use biometric features. Face detection is not straightforward as long as it has lots of dissimilarity of image appearance. Some challenges are required to be resolved to improve the detection process. These challenges include environmental constraints, device specific constraints and the facial feature constraints. Here in our paper we present a modified method to enrich face detection by using combination of Haar cascade files using skin detection, eye detection and nose detection. Our proposed system has been evaluated using Wild database. The experimental results have shown that the proposed system can achieve accuracy of detection up to 96%. Also, here we compared the proposed system with the other face detection systems and the success rate of the proposed system is better than the considered systems.

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Section: Skin Detectionmentioning

confidence: 99%

A Robust and Efficient System to Detect Human Faces Based on Facial Features

Ali

El‐Hafeez

Mohany

2019

AJRCoS

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“…Face detection consists in identifying which parts of a still image correspond to faces, as illustrated in Figure 3. See [133] for a recent review of the field. Jung et al note that current face detection methods are efficient mainly on front views of the faces [118]: this is a strong limitation in our context, as a character can be filmed under a variety of angles.…”

Section: Visual Narrativesmentioning

confidence: 99%

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder.This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

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“…where Y component represents the luminance values that ranged from 16 to 235, while C b and C r components are the chrominance values that ranged from 16 to 240 [20,21]. Secondly, the chest region where the cardiopulmonary motion is most pronounced was automatically selected using a body joint estimation approach, called OpenPose [22].…”

Section: System Framework and Data Analysismentioning

confidence: 99%

“…where component represents the luminance values that ranged from 16 to 235, while and components are the chrominance values that ranged from 16 to 240 [20,21].…”

Section: System Framework and Data Analysismentioning

confidence: 99%

Life Signs Detector Using a Drone in Disaster Zones

et al. 2019

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In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.

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Face detection techniques: a review

Cited by 256 publications

References 27 publications

A Robust and Efficient System to Detect Human Faces Based on Facial Features

A Robust and Efficient System to Detect Human Faces Based on Facial Features

Extraction and Analysis of Fictional Character Networks

Life Signs Detector Using a Drone in Disaster Zones

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