Human and Vehicle-Driver Drowsiness Detection by Facial Expression

Hachisuka, Satori

doi:10.1109/icbake.2013.89

Cited by 21 publications

(9 citation statements)

References 14 publications

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“…Estos pará-metros son claves a la hora de determinar si se está en somnolencia o vigilia. Bostezar repetidamente, dificultad para mantener la mirada en cierto punto, dificultad para mantener la cabeza erguida, el movimiento de las cejas, y dificultad para enfocar son parámetros visibles y contundentes que precisan si una persona está en estado de somnolencia (Hachisuka, 2013). Aunque recientes estudios han demostrado que estos factores no siempre se presentan en trabajadores y conductores que mantienen un estado de vigilia forzado, pues debido a que ellos deben cumplir con determinadas labores y cumplir con una jornada laboral, a veces su estado de somnolencia no puede ser apreciado por métodos de inspección visual (Yang, y otros, 2009).…”

Section: 1sistemas De Visión Por Computadorunclassified

“…Veremos cómo desde hace 60 años se tenía la percepción de llevar a cabo este monitoreo, pero solo se han logrado desarrollos importantes desde los últimos 20 años, y además enmarcaremos nuestra futura investigación en esta área para así acercarnos más a un sistema más eficiente de detección de estado de somnolencia para conductores (Flores, y otros, 2011) (Hachisuka, 2013) (Jo, y otros, 2014).…”

Section: Introductionunclassified

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Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro

Garcés

Salgado

Cruz

et al. 2015

Ingeniería y Región

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_____________________________________________________________________________________ ResumenMuchas son las causas de accidentes de tránsito e industriales a nivel mundial, algunas de estos suceden por errores humanos y otros por fallas mecánicas. El hombre en su afán de proteger vidas, ha inventado sistemas que minimicen el impacto de estos accidentes, pero más que disminuir el daño ahora se piensa en la prevención de los mismos. Uno de los errores humanos más comunes que terminan en accidentes es cuando el conductor u operario industrial es víctima de fatiga y/o somnolencia. Las investigaciones sobre este tema que comenzaron hace 60 años han dejado a través del tiempo novedosos sistemas que permiten determinar el estado de somnolencia de las personas usando técnicas de visión por computador, y un naciente interés por el análisis de señales cerebrales que determinan de forma aún más precisa las diferentes etapas del sueño. En este trabajo se presentan cada una de las técnicas empleadas hasta el momento para detectar somnolencia y su importancia como sistema activo de prevención de accidentes de tránsito e industriales.Palabras clave: Somnolencia, Visión por computador, señales cerebrales, sistemas activos.Traffic and industrial accidents are caused by many different factors. Some are due to human error and others, mechanical failure. In an effort to protect lives, many systems have been invented to minimize the impact of such accidents; however, prevention is now understood as more important than minimizing damage after the accident has already taken place. Among the most common human errors that lead to accidents is when a driver or industrial operator is overtired, fatigued or feeling drowsy. Research on this subject began 60 years ago and has developed numerous innovative time systems for detecting states of drowsiness in people using computer vision techniques. There has also been a rising interest in the analysis of brain signals that very precisely determine the different stages of sleep. This paper will review each of the techniques used to detect drowsiness and their importance as active prevention systems for traffic and industrial accidents.

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Section: 1sistemas De Visión Por Computadorunclassified

Section: Introductionunclassified

Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro

Garcés

Salgado

Cruz

et al. 2015

Ingeniería y Región

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show abstract

“…Literatürde; şizofreni hastalarının ruhsal durumlarının belirlenmesinde [10][11][12], hırsızlara ve kötü niyetli kişilere karşı güvenliği sağlamada [13,14], yeni doğan bebekler için ağrı tespitinde [15,16] yüz ifadeleri kullanılmıştır. Ayrıca sürücülerin yorgunluk ve uykulu olma durumlarının algılamasında [17], güvenli sürüş davranışlarının tahmin edilmesinde [18] ve otomatik müzik çalma listesi oluşturmada [19,20] yüz ifadelerini kullanan çalışmalar vardır. Bu çalışmada, yüz ifade analizi ile ilgili detaylı bir tarama gerçekleştirilmiştir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

Bayrakdar¹,

Akgün²,

Yücedağ³

2015

SAUFenBilDer

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ÖZYüz ifadeleri, duygulara kişilerarası iletişimde anlam bütünlüğü kazandıran evrensel işaretlerdir. Aynı zamanda yüz ifadelerindeki değişimler, duygu psikolojisinde en önemli ipuçları olarak kabul edilmektedir. Yüz ifade analizinin; insan davranışlarının analizi, insan-insan etkileşimi ve insan-bilgisayar etkileşimi gibi çok geniş bir uygulama yelpazesi vardır. İnsanlar yüz ifadelerini tanımada ve yorumlamada herhangi bir sorun yaşamazken bu işlem bilgisayarlarda zorlu bir problem olarak karşımıza çıkmaktadır. Fakat özellikle insan-bilgisayar etkileşiminde yaşanan gelişmelerle birlikte, insanlara ait duyguların bilgisayarlar tarafından anlaşılması elzem bir konu haline gelmiştir. Bunun yanında güvenlik, psikoloji, sağlık, robotik ve sanal gerçeklilik gibi birçok çalışma alanında da yüz ifadelerinin analizi ve tanıması giderek yaygınlaşmıştır. Bu nedenlerden dolayı, yüz ifadelerinin hızlı bir şekilde analiz edilmesi ve analiz edilen yüz ifadelerinin doğru olarak tanınması farklı uygulama alanlarında birçok yazılım sistemi için kritik bir rol oynamaktadır. Bu çalışmada, yüz ifadelerinin tanıması ile ilgili literatürde yapılan çalışmalar incelenmiş ve yüz ifadelerinin analizinde kullanılan bazı yöntemlere yer verilmiştir.Anahtar Kelimeler: yüz ifade analizi, yüz ifadesi tanıma, görüntü işleme A survey on automatic analysis of facial expressions ABSTRACT Facial expressions are universal symbols of emotions that provide cohesion to interpersonal communication. At the same time, the changes in facial expressions are considered to be the most important hints in the psychology of emotion. Facial expression analysis has widespread range of application in areas such as analysis of human behaviors, human-human interaction and human-computer interaction. While humans do not have any difficulty for recognizing and interpreting facial expressions, it is challenging problem for the computers. However, especially with advances in human-computer interaction, understanding the human emotions by computer has become an indispensable issue. Furthermore, analysis and recognition of facial expressions has pervaded in various areas such as security, psychology, health, robotics and virtual reality. For these reasons, the analyzing of facial expressions quickly and the recognition of facial expressions correctly according to the analyzed facial expressions play a critical role for many software systems in different applications. In this study, the methods used in the analysis of facial expressions and the studies in the literature on emotion recognition were investigated.

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“…There are different learning methods are used to detect the fatigue like supervised, unsupervised and reinforcement learning. For example, itenderpal singh et al [6] used the viola jones algorithm to detect the face which discovering the feedback picture method of sub screen effective at finding features. …”

Section: Hybrid Technique To Detect Drowsinessmentioning

confidence: 99%

“…The biomedical signal handling have already been applying to solve biochemical sciences issues, such as for example mindbrain imaging technology, examining cranial nerves productive dynamic distributed and further brain information processing mechanism. Leng, Lee Boon et al [6] driver drowsiness detection system had been developed as mobile device application such as Percentage of Eye Closure (PERCLOS) measured by using mobile device camera. Gang Li et al [7] used wearable EEG process which is made up of a Bluetooth-enabled EEG headband and a commercial smart watch was applied to evaluate the model in a real-time way.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Driver Drowsiness Detection System by using Soft Computing

Bhagat¹,

Banga²

2017

IJCA

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This report presents an automatic drowsy driver recognition and accident prevention system that is based on facial expressions changes. The key reason of traffic incidents could be because of drowsiness due to the number of years driving. Examining the facial expression can provide offer the prediction of driver's drowsiness to create the caution for the driver. Therefore, this report presents the drowsiness recognition approach for applying in vehicles. Our approach is accomplished by having a driver's facial image, searching the facial characteristics by image handling and analyzing the driver's drowsiness stage by utilizing hybrid technique.

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Human and Vehicle-Driver Drowsiness Detection by Facial Expression

Cited by 21 publications

References 14 publications

Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro

Sistemas de detección de somnolencia en conductores: inicio, desarrollo y futuro

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

Analysis of Driver Drowsiness Detection System by using Soft Computing

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