A Novel Machine Vision-Based 3D Facial Action Unit Identification for Fatigue Detection
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[1] Shree K. Nayar,et al. Shape from focus: an effective approach for rough surfaces , 1990, Proceedings., IEEE International Conference on Robotics and Automation.
[2] Christer Ahlstrom,et al. The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator , 2017 .
[3] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[4] Jie Lin,et al. Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State , 2017, IEEE Transactions on Intelligent Transportation Systems.
[5] Andrew W. Fitzgibbon,et al. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.
[6] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Carryl L. Baldwin,et al. Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies , 2009 .
[9] Wei Sun,et al. A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels , 2017, IEEE Transactions on Intelligent Transportation Systems.
[10] Jacob Scharcanski,et al. Yawning Detection Using Embedded Smart Cameras , 2016, IEEE Transactions on Instrumentation and Measurement.
[11] Ping-Sing Tsai,et al. Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[12] Keiichi Uchimura,et al. Driver Inattention Monitoring System for Intelligent Vehicles: A Review , 2009, IEEE Transactions on Intelligent Transportation Systems.
[13] Ying Wu,et al. Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety , 2017, Sensors.
[14] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[15] Alex Pentland,et al. Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Muhammad Tahir Khan,et al. Non intrusive selective facial feature tracking: A fuzzy control approach , 2018, 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE).
[17] Hideki Hayakawa. Photometric stereo under a light source with arbitrary motion , 1994 .
[18] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[19] W. Harris. Fatigue, Circadian Rhythm, and Truck Accidents , 1977 .
[20] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[21] Jean-Denis Durou,et al. Solving Uncalibrated Photometric Stereo Using Total Variation , 2014, Journal of Mathematical Imaging and Vision.
[22] J. Cohn,et al. A Psychometric Evaluation of the Facial Action Coding System for Assessing Spontaneous Expression , 2001 .
[23] Shahram Azadi,et al. Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss , 2014, Sensors.
[24] Gwen Littlewort,et al. Drowsy Driver Detection Through Facial Movement Analysis , 2007, ICCV-HCI.
[25] Yvonne Tran,et al. A controlled investigation into the psychological determinants of fatigue , 2006, Biological Psychology.
[26] Stefanos Zafeiriou,et al. Face Recognition and Verification Using Photometric Stereo: The Photoface Database and a Comprehensive Evaluation , 2013, IEEE Transactions on Information Forensics and Security.
[27] Yongtian Wang,et al. Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery , 2010, ACCV.
[28] Robert J. Woodham,et al. Photometric method for determining surface orientation from multiple images , 1980 .
[29] Gwen Littlewort,et al. Discrimination of Moderate and Acute Drowsiness Based on Spontaneous Facial Expressions , 2010, 2010 20th International Conference on Pattern Recognition.
[30] Lyndon Smith,et al. Innovative machine vision technique for 2D/3Dcomplex and irregular surfaces modelling , 2012 .
[31] Girish Chowdhary,et al. Real‐time detection of distracted driving based on deep learning , 2018, IET Intelligent Transport Systems.
[32] T. Åkerstedt,et al. Validation of the Karolinska sleepiness scale against performance and EEG variables , 2006, Clinical Neurophysiology.
[33] Emmanuelle Diaz,et al. Detection and prediction of driver drowsiness using artificial neural network models. , 2017, Accident; analysis and prevention.
[34] Melvyn L. Smith,et al. Dynamic photometric stereo - a new technique for moving surface analysis , 2005, Image Vis. Comput..
[35] Gwen Littlewort,et al. Automated drowsiness detection for improved driving safety , 2008 .
[36] Shahzad Anwar,et al. Facial feature detection: A facial symmetry approach , 2017, 2017 5th International Symposium on Computational and Business Intelligence (ISCBI).
[37] David J. Kriegman,et al. The Bas-Relief Ambiguity , 2004, International Journal of Computer Vision.
[38] B. S. Manjunath,et al. A robust method for detecting image features with application to face recognition and motion correspondence , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.
[39] Ye Sun,et al. An Innovative Nonintrusive Driver Assistance System for Vital Signal Monitoring , 2014, IEEE Journal of Biomedical and Health Informatics.
[40] Shahzad Anwar,et al. Driver Fatigue Detection Systems: A Review , 2019, IEEE Transactions on Intelligent Transportation Systems.
[41] Melvyn L. Smith,et al. Real-time recovery of moving 3D faces for emerging applications , 2013, Comput. Ind..