Face Recognition and Drunk Classification Using Infrared Face Images

Hermosilla, Gabriel; Verdugo, José Luis; Farías, Gonzalo; Vera, Esteban; Pizarro, Francisco; Machuca, Margarita

doi:10.1155/2018/5813514

Cited by 34 publications

(16 citation statements)

References 21 publications

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“…Recognizing whether drivers are DUI of alcohol can also be achieved using a camera that acquires IR images [ 242 , 243 , 244 ]. For an intoxicated person, vessels on the forehead become more active so that, in an IR image, the intensities of the pixels in this region are affected accordingly.…”

Section: State 5: Under the Influencementioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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Section: State 5: Under the Influencementioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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“…Considering the match between the anchor and the loss function, the location loss function remains the same as the distance equation used in clustering. The first stage loss function is defined as equations (1)- (3).…”

Section: Two-stage Weight Lossmentioning

confidence: 99%

“…Face detection, the basis of face alignment [1,2], face recognition [3,4], facial expression analysis [5,6], and other related facial problems, has always been a hot issue and widely applied in terms of computer vision. As the major breakthrough for face detection, Viola-Jones [7] used the handcrafted features for cascading detection.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Single Stage Network for Face Detection

Hui

Wang

Huang

et al. 2021

Wireless Communications and Mobile Computing

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Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.

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“…e methodology was evaluated using the Pontificia Universidad Católica de Valparaíso-Drunk ermal Face database (PUCV-DTF) [8]. It consists of 46 participants: 40 men and 6 women with an average of 24 years (between 18 and 29 years).…”

Section: Ermal Face Databasementioning

confidence: 99%

Facial Recognition for Drunk People Using Thermal Imaging

Sancen-Plaza

Contreras-Medina

Barranco-Gutiérrez

et al. 2020

Mathematical Problems in Engineering

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Face recognition using thermal imaging has the main advantage of being less affected by lighting conditions compared to images in the visible spectrum. However, there are factors such as the process of human thermoregulation that cause variations in the surface temperature of the face. ese variations cause recognition systems to lose effectiveness. In particular, alcohol intake causes changes in the surface temperature of the face. It is of high relevance to identify not only if a person is drunk but also their identity. In this paper, we present a technique for face recognition based on thermal face images of drunk people. For the experiments, the Pontificia Universidad Católica de Valparaíso-Drunk ermal Face database (PUCV-DTF) was used. e recognition system was carried out by using local binary patterns (LBPs). e LBP features were obtained from the bioheat model from thermal image representation and a fusion of thermal images and a vascular network extracted from the same image. e feature vector for each image is formed by the concatenation of the LBP histogram of the thermogram with an anisotropic filter and the fused image, respectively. e proposed technique has an average percentage of 99.63% in the Rank-10 cumulative classification; this performance is superior compared to using LBP in thermal images that do not use the bioheat model.

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Face Recognition and Drunk Classification Using Infrared Face Images

Cited by 34 publications

References 21 publications

Survey and Synthesis of State of the Art in Driver Monitoring

Survey and Synthesis of State of the Art in Driver Monitoring

A Multilevel Single Stage Network for Face Detection

Facial Recognition for Drunk People Using Thermal Imaging

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