Driver Facial Detection Across Diverse Road Conditions


Siti Shofiah(1*); Eko Sediyono(2); Zainal Arifin Hasibuan(3); Budhi Kristianto(4); Santo Setiawan(5); Raka Pratindy(6); M. Iman Nur Hakim(7); Faris Humami(8);

(1) Universitas Kristen Satya Wacana
(2) Universitas Kristen Satya Wacana
(3) Universitas Dian Nuswantoro
(4) Universitas Kristen Satya Wacana
(5) Politeknik Keselamatan Transportasi Jalan
(6) Politeknik Keselamatan Transportasi Jalan
(7) Politeknik Keselamatan Transportasi Jalan
(8) Politeknik Keselamatan Transportasi Jalan
(*) Corresponding Author

  

Abstract


This study emphasizes the importance of facial detection for improving road safety through driver behavior analysis. Its employs quantitative methodology to underscore the importance of facial detection in enhancing road safety through driver behavior analysis. The research utilizes the Python programming language and applies the Haar cascade method to investigate how environmental factors such as low light, shadows, and lighting changes influence the reliability of facial detection. Employing the AdaBoost algorithm, the study achieves face detection rates exceeding 95%. Practical testing with an ASUS A416JA laptop and Raspberry Pi under varied lighting conditions and distances demonstrates optimal performance in detecting faces between 30 cm and 70 cm, with reduced efficacy outside this range, particularly in low light conditions and at night. Challenges identified include decreased performance in low light conditions, emphasizing the need for improved algorithmic calibration and enhancement. Future research directions involve refining detection algorithms to effectively handle diverse environmental conditions and integrating advanced machine learning techniques, thereby enhancing the accuracy of driver behavior analysis in real-world scenarios and contributing to advancements in road safety


Keywords


Driver Fatigue; Facial Detection Accuracy; Road Safety; Safety Enhancements

  
  

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doi  https://doi.org/10.33096/ilkom.v16i2.1996.108-114 		  

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References


Y. Sugimoto and C. Sauer, “Effectiveness estimation method for advanced driver assistance system and its application to collision mitigation brake system,” … on the Enhanced Safety of Vehicles. www-esv.nhtsa.dot.gov, 2005.

Y. Sun, P. Yan, Z. Li, J. Zou, and D. Hong, “Driver fatigue detection system based on colored and infrared eye features fusion,” Comput. Mater. Contin., vol. 63, no. 3, pp. 1563–1574, 2020, doi: 10.32604/CMC.2020.09763.

S. Dey, S. A. Chowdhury, S. Sultana, M. A. Hossain, M. Dey, and S. K. Das, “Real Time Driver Fatigue Detection Based on Facial Behaviour along with Machine Learning Approaches,” 2019 IEEE Int. Conf. Signal Process. Information, Commun. Syst. SPICSCON 2019, pp. 135–140, 2019, doi: 10.1109/SPICSCON48833.2019.9065120.

S. Said, S. AlKork, T. Beyrouthy, M. Hassan, O. E. Abdellatif, and M. Fayek Abdraboo, “Real time eye tracking and detection- A driving assistance system,” Adv. Sci. Technol. Eng. Syst., vol. 3, no. 6, pp. 446–454, 2018, doi: 10.25046/aj030653.

M. Chakraborty and A. N. H. Aoyon, “Implementation of Computer Vision to detect driver fatigue or drowsiness to reduce the chances of vehicle accident,” 1st Int. Conf. Electr. Eng. Inf. Commun. Technol. ICEEICT 2014, 2014, doi: 10.1109/ICEEICT.2014.6919054.

M. Khatun, R. Jung, and M. Glaß, “Scenario-based collision detection using machine learning for highly automated driving systems,” Syst. Sci. Control Eng., vol. 11, no. 1, 2023, doi: 10.1080/21642583.2023.2169384.

KNKT, “Buku statistik investigasi kecelakaan transportasi knkt 2022,” no. 5, 2022.

A. F. N. Sarjan, F. F. Salsabila, and A. Rofaida, “Sosialisasi Keselamatan Berlalu Lintas untuk Mengurangi Angka Kejadian Kecelakaan Bagi Pelajar di SMAN 1 Selong Kabupaten Lombok Timur,” Unram J. Community Serv., vol. 3, no. 4, pp. 120–122, 2022, doi: 10.29303/ujcs.v3i4.167.

K. Zaman et al., “A novel driver emotion recognition system based on deep ensemble classification,” Complex Intell. Syst., vol. 9, no. 6, pp. 6927–6952, 2023, doi: 10.1007/s40747-023-01100-9.

Z. Cui, H. M. Sun, R. N. Yin, L. Gao, H. Bin Sun, and R. S. Jia, “Real-time detection method of driver fatigue state based on deep learning of face video,” Multimed. Tools Appl., vol. 80, no. 17, pp. 25495–25515, 2021, doi: 10.1007/s11042-021-10930-z.

Y. Lu, X. Fu, E. Guo, and F. Tang, “XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarity,” IEEE Access, vol. 9, pp. 21921–21938, 2021, doi: 10.1109/ACCESS.2021.3055551.

B. Akrout and S. Fakhfakh, “How to Prevent Drivers before Their Sleepiness Using Deep Learning-Based Approach,” Electron., vol. 12, no. 4, pp. 1–19, 2023, doi: 10.3390/electronics12040965.

M. H. Alkinani, W. Z. Khan, and Q. Arshad, “Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges,” IEEE Access, vol. 8, pp. 105008–105030, 2020, doi: 10.1109/ACCESS.2020.2999829.

W. Shariff et al., “Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection,” IEEE Open J. Veh. Technol., vol. 4, no. October, pp. 836–848, 2023, doi: 10.1109/OJVT.2023.3325656.

S. F. Zhao, W. Guo, and C. W. Zhang, “Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State,” J. Adv. Transp., vol. 2017, 2017, doi: 10.1155/2017/9509213.

M. H. Alkinani, W. Z. Khan, and Q. Arshad, “Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges,” IEEE Access, vol. 8, pp. 105008–105030, 2020, doi: 10.1109/ACCESS.2020.2999829.

J. Hollósi, Á. Ballagi, G. Kovács, S. Fischer, and V. Nagy, “Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions,” Computers, vol. 13, no. 3, 2024, doi: 10.3390/computers13030066.

S. Palaniswamy and S. Tripathi, “Emotion recognition from facial expressions using images with pose, illumination and age variation for human-computer/robot interaction,” J. ICT Res. Appl., vol. 12, no. 1, pp. 14–34, 2018, doi: 10.5614/itbj.ict.res.appl.2018.12.1.2.

S. Akshay, S. Mandara, and A. G. Rao, “Facial expression recognition using compressed images,” Int. J. Recent Technol. Eng., vol. 8, no. 2, pp. 1741–1745, 2019, doi: 10.35940/ijrte.B1041.078219.

Y. Shang, M. Yang, J. Cui, L. Cui, Z. Huang, and X. Li, “Driver Emotion and Fatigue State Detection Based on Time Series Fusion,” Electron., vol. 12, no. 1, 2023, doi: 10.3390/electronics12010026.

K. Karilingappa, D. Jayadevappa, and S. Ganganna, “Human emotion detection and classification using modified Viola-Jones and convolution neural network,” IAES Int. J. Artif. Intell., vol. 12, no. 1, pp. 79–86, 2023, doi: 10.11591/ijai.v12.i1.pp79-86.

A. H. T. Al-Ghrairi, A. A. Mohammed, and E. Z. Sameen, “Face detection and recognition with 180 degree rotation based on principal component analysis algorithm,” IAES Int. J. Artif. Intell., vol. 11, no. 2, pp. 593–602, 2022, doi: 10.11591/ijai.v11.i2.pp593-602.

M. E. Irhebhude, A. O. Kolawole, and H. K. Goma, “A Gender Recognition System Using Facial Images with High Dimensional Data,” Malaysian J. Appl. Sci., vol. 6, no. 1, pp. 27–45, 2021, doi: 10.37231/myjas.2021.6.1.275.

K. Karilingappa, D. Jayadevappa, and S. Ganganna, “Human emotion detection and classification using modified Viola-Jones and convolution neural network,” IAES Int. J. Artif. Intell., vol. 12, no. 1, pp. 79–86, 2023, doi: 10.11591/ijai.v12.i1.pp79-86.

R. Robin, A. Handinata, and W. Chandra, “Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar,” J. Comput. Networks, Archit. High Perform. Comput., vol. 3, no. 2, pp. 213–222, 2021, doi: 10.47709/cnahpc.v3i2.1058.

R. Gawande and S. Badotra, “Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 782–795, 2022, doi: 10.14569/IJACSA.2022.0130693.

B. I. Hussain and M. Rafi, “A Secured Biometric Authentication with Hybrid Face Detection and Recognition Model,” Int. J. Intell. Eng. Syst., vol. 16, no. 3, pp. 48–61, 2023, doi: 10.22266/ijies2023.0630.04.

S. Sunardi, A. Yudhana, and S. A. Wijaya, “Penerapan Metode Median Filtering untuk Optimasi Deteksi Wajah pada Foto Digital,” J. Innov. Inf. Technol. Appl., vol. 4, no. 1, pp. 51–60, 2022, doi: 10.35970/jinita.v4i1.1214.

K. S. R. Murthy, B. Siddineni, V. K. Kompella, K. Aashritha, B. H. Sri Sai, and V. M. Manikandan, “An Efficient Drowsiness Detection Scheme using Video Analysis,” Int. J. Comput. Digit. Syst., vol. 11, no. 1, pp. 573–581, 2022, doi: 10.12785/ijcds/110146.

B. Akrout and W. Mahdi, “A novel approach for driver fatigue detection based on visual characteristics analysis,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 1, pp. 527–552, 2023, doi: 10.1007/s12652-021-03311-9.

P. Sowmyashree and J. Sangeetha, “Multistage End-to-End Driver Drowsiness Alerting System,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 4, pp. 464–473, 2023, doi: 10.14569/IJACSA.2023.0140452.

I. R. Adochiei et al., “Drivers’ drowsiness detection and warning systems for critical infrastructures,” 2020 8th E-Health Bioeng. Conf. EHB 2020, pp. 14–17, 2020, doi: 10.1109/EHB50910.2020.9280165.

A. K. Biswal, D. Singh, B. K. Pattanayak, D. Samanta, and M. H. Yang, “IoT-based smart alert system for drowsy driver detection,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/6627217.

A. E. Widjaja, H. Hery, and D. Habsara Hareva, “The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN,” INTENSIF J. Ilm. Penelit. dan Penerapan Teknol. Sist. Inf., vol. 5, no. 1, pp. 1–12, 2021, doi: 10.29407/intensif.v5i1.14435.

V. Valsan A, P. P. Mathai, and I. Babu, “Monitoring driver’s drowsiness status at night based on computer vision,” Proc. - IEEE 2021 Int. Conf. Comput. Commun. Intell. Syst. ICCCIS 2021, pp. 989–993, 2021, doi: 10.1109/ICCCIS51004.2021.9397180.

T. Meireles and F. Dantas, “A low-cost prototype for driver fatigue detection,” Multimodal Technol. Interact., vol. 3, no. 1, 2019, doi: 10.3390/mti3010005.

W. Kim, W. S. Jung, and H. K. Choi, “Lightweight driver monitoring system based on multi-task mobilenets,” Sensors (Switzerland), vol. 19, no. 14, 2019, doi: 10.3390/s19143200.

F. Wang, X. Chen, D. Wang, and B. Yang, “An improved image-based iris-tracking for driver fatigue detection system,” Chinese Control Conf. CCC, pp. 11521–11526, 2017, doi: 10.23919/ChiCC.2017.8029198.

S. Said, S. AlKork, T. Beyrouthy, M. Hassan, O. E. Abdellatif, and M. Fayek Abdraboo, “Real time eye tracking and detection- A driving assistance system,” Adv. Sci. Technol. Eng. Syst., vol. 3, no. 6, pp. 446–454, 2018, doi: 10.25046/aj030653.

W. Alkishri, A. Abualkishik, and M. Al-Bahri, “Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection,” Appl. Comput. Intell. Soft Comput., vol. 2022, 2022, doi: 10.1155/2022/9551203.

B. Neupane, T. Horanont, P. Pattarapongsin, and A. Thapa, “Robust and Scalable Real-Time Vehicle Classification and Tracking: a Case Study of Thailand,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 10, no. 4/W3-2022, pp. 181–187, 2022, doi: 10.5194/isprs-annals-X-4-W3-2022-181-2022.

V. V. Arlazarov, J. S. Voysyat, D. P. Matalov, D. P. Nikolaev, and S. A. Usilin, “Evolution of the Viola-Jones object detection method: A survey,” Bull. South Ural State Univ. Ser. Math. Model. Program. Comput. Softw., vol. 14, no. 4, pp. 5–23, 2021, doi: 10.14529/mmp210401.

R. Z. Ye, A. Subramanian, D. Diedrich, H. Lindroth, B. Pickering, and V. Herasevich, “Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib,” J. Imaging, vol. 8, no. 12, 2022, doi: 10.3390/jimaging8120330.

S. Sunardi, A. Yudhana, and S. A. Wijaya, “Face Detection Analysis of Digital Photos Using Mean Filtering Method,” Int. J. Artif. Intell. Res., vol. 6, no. 2, 2022, doi: 10.29099/ijair.v6i2.307.

I. R. Adochiei et al., “Drivers’ drowsiness detection and warning systems for critical infrastructures,” 2020 8th E-Health Bioeng. Conf. EHB 2020, pp. 14–17, 2020, doi: 10.1109/EHB50910.2020.9280165.

V. R. Reddy Chirra, S. R. Uyyala, and V. K. Kishore Kolli, “Deep CNN: A machine learning approach for driver drowsiness detection based on eye state,” Rev. d’Intelligence Artif., vol. 33, no. 6, pp. 461–466, 2019, doi: 10.18280/ria.330609.

R. Chaudhury, “Adaboost classifier for face detection using viola jones algorithm,” 2022.

K. D. Ismael and S. Irina, “Face recognition using Viola-Jones depending on Python,” Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1513–1521, 2020, doi: 10.11591/ijeecs.v20.i3.pp1513-1521.

S. Sarhan, A. A. Nasr, and M. Y. Shams, “Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization,” Comput. Intell. Neurosci., vol. 2020, 2020, doi: 10.1155/2020/8821868.


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