Facial feature point detection: A comprehensive survey

Wang, Nannan; Gao, Xinbo; Tao, Dacheng; Yang, Heng; Li, Xuelong

doi:10.1016/j.neucom.2017.05.013

Cited by 183 publications

(122 citation statements)

References 178 publications

(279 reference statements)

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“…Much of this progress can be attributed to the release of large annotated datasets of facial landmarks (Sagonas et al 2013b, a;Zhu and Ramanan 2012;Le et al 2012;Belhumeur et al 2013;Köstinger et al 2011) and very recently the area of facial landmark localisation has become extremely competitive with recent works including Xiong and De la Torre (2013), Ren et al (2014), Kazemi and Sullivan (2014), Zhu et al (2015), Tzimiropoulos (2015). For a recent evaluation of facial landmark localisation methods the interested reader may refer to the survey by Wang et al (2014) and to the results of the 300 W competition by Sagonas et al (2015). Finally, face recognition and verification are extremely popular lines of research.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…For such intruders, it is straightforward to disconnect a CCTV camera surveillance, which has an indirect connection to the digital video recorder and a database server residing at home. Therefore, there is a need to modify existing systems [5][6][7][8][9][10][11] [13][14][15][16][17][18] and propose an intelligent approach that can not only provide unsupervised human activity monitoring, but can also stop an on-going theft by notifying the house-owner at the earliest opportunity. All legacy systems work on the premise of object detection, object motion detection and tracking.…”

Section: Necessity Of a New Iot-based Theft Systemmentioning

confidence: 99%

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Pandya¹,

Ghayvat²,

Kotecha³

et al. 2018

Preprint

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Abstract:The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.

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“…State-of-the-art methods include tree models [65,66], DPM [67], SDM [57], explicit shape regression [68] or learning local binary features [69]. A comprehensive survey of facial feature point detection can be seen here [70]. All the above listed research suffers more or less from a lack of verification and performance analysis with a realistic variation in lighting conditions.…”

Section: Face and Facial Landmarks Detectionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

101

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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Facial feature point detection: A comprehensive survey

Cited by 183 publications

References 178 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring, Smart Home Security and Large Video Data Handling for Wellness Protocol

Driver Distraction Using Visual-Based Sensors and Algorithms

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