Driver fatigue recognition based on facial expression analysis using local binary patterns

Zhang, Yan; Chen, Hua

doi:10.1016/j.ijleo.2015.08.185

Cited by 83 publications

(44 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(3) These features are fed into a classifier, such as support vector machine (SVM) or nearest neighbor (NN), to classify each expression (Kumari, Rajesh, and Pooja, 2015). Using these methods, many studies have attempted to better understand the causes of unsafe behaviors such as fatigue (Zhang and Hua, 2015), aggressive driving (Moriyama, Abdelaziz, and Shimomura, 2012), frustration (Abdíc et al, 2016), cognitive load (Fridman et al, 2018), and so on. Some of the studies, such as the one conducted by Fridman and colleagues (2019), have only used gaze and eye tracking, while others have looked at complete facial analysis (Fridman et al, 2019).…”

Section: Background Studymentioning

confidence: 99%

A Multimodal Approach for Monitoring Driving Behavior and Emotions

Tavakoli¹,

Balali²,

Heydarian³

2020

View full text Add to dashboard Cite

Founded in 1991, the Mineta Transportation Institute (MTI), an organized research and training unit in partnership with the Lucas College and Graduate School of Business at San José State University (SJSU), increases mobility for all by improving the safety, efficiency, accessibility, and convenience of our nation's transportation system. Through research, education, workforce development, and technology transfer, we help create a connected world. MTI leads the four-university. MTI leads the four-university California State University Transportation Consortium funded by the State of California through Senate Bill 1. MTI's transportation policy work is centered on three primary responsibilities: MINETA TRANSPORTATION INSTITUTE Research MTI works to provide policy-oriented research for all levels of government and the private sector to foster the development of optimum surface transportation systems. Research areas include: bicycle and pedestrian issues; financing public and private sector transportation improvements; intermodal connectivity and integration; safety and security of transportation systems; sustainability of transportation systems; transportation / land use / environment; and transportation planning and policy development. Certified Research Associates conduct the research. Certification requires an advanced degree, generally a Ph.D., a record of academic publications, and professional references. Research projects culminate in a peer-reviewed publication, available on TransWeb, the MTI website (http://transweb.sjsu.edu). Education The Institute supports education programs for students seeking a career in the development and operation of surface transportation systems. MTI, through San José State University, offers an AACSBaccredited Master of Science in Transportation Management and graduate certificates in Transportation Management, Transportation Security, and High-Speed Rail Management that serve to prepare the nation's transportation managers for the 21st century. With the active assistance of the California Department of Transportation (Caltrans), MTI delivers its classes over a state-of-the-art videoconference network throughout the state of California and via webcasting beyond, allowing working transportation professionals to pursue an advanced degree regardless of their location. To meet the needs of employers seeking a diverse workforce, MTI's education program promotes enrollment to under-represented groups. Information and Technology Transfer MTI utilizes a diverse array of dissemination methods and media to ensure research results reach those responsible for managing change. These methods include publication, seminars, workshops, websites, social media, webinars, and other technology transfer mechanisms. Additionally, MTI promotes the availability of completed research to professional organizations and journals and works to

show abstract

Section: Background Studymentioning

confidence: 99%

A Multimodal Approach for Monitoring Driving Behavior and Emotions

Tavakoli¹,

Balali²,

Heydarian³

2020

View full text Add to dashboard Cite

show abstract

“…[14] The driver fatigue detection is achieved by using recognizing yawning and monitoring the driver"s country by using using Viola Jones algorithm. [15] The extraction of the driver"s face from the unique photo is a lengthy and sluggish system and it is overcome by way of adopting fast face detection algorithm. Local Binary Patterns is used to find facial expression.…”

Section: Related Workmentioning

confidence: 99%

666 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A4548119119/2019©BEIESP DOI: 10.35940/ijitee.A4548.119119 Journal Website: www.ijitee.org Heavy Eyed Driver Detection System

Agnal*,

Balaji,

Reddy

et al. 2019

IJITEE

View full text Add to dashboard Cite

Driver fatigue is a great element in a massive variety of car accidents. Drowsy driver warning gadgets can form the foundation of the machine to maybe minimize the accidents associated with drowsiness. This paper uses a web cam for picture capturing. A web camera is connected to PC, and images are received and processed with the aid of mat lab using image processing. By setting the camera interior the car, we can monitor the face of the driver and look for the eye actions that say whether the driver is in a situation to drive. If the gadget detects that the driver is drowsy, a warning alert is issued. If eyes are detected as shut for too lengthy a beep sound is produced and as a result alerting the driver.

show abstract

“…Zhao et al [3] used the Deep Belief Network (DBN) to extract different facial fatigue features from the dataset and respectively verified the accuracy of driver drowsiness detection. Zhang et al [4] used the local binary pattern (LBP) and support vector machine (SVM) fatigue expression reorganization algorithm to estimate the fatigue degree of drivers. However, this detection method based on a single facial feature has some limitations in robustness and reliability, and does not consider the time change characteristics of the driver's drowsiness, thereby reducing the recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

2020

View full text Add to dashboard Cite

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

show abstract

Driver fatigue recognition based on facial expression analysis using local binary patterns

Cited by 83 publications

References 25 publications

A Multimodal Approach for Monitoring Driving Behavior and Emotions

A Multimodal Approach for Monitoring Driving Behavior and Emotions

666 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number A4548119119/2019©BEIESP DOI: 10.35940/ijitee.A4548.119119 Journal Website: www.ijitee.org Heavy Eyed Driver Detection System

Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

Contact Info

Product

Resources

About