Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection
DOI:
https://doi.org/10.30865/mib.v8i1.7167Keywords:
Decision Tree, Drowsiness Detection, Haar Cascade, Region of Interest, Yolo-FaceAbstract
Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
References
M. Ramzan, H. U. Khan, S. M. Awan, A. Ismail, M. Ilyas, and A. Mahmood, “A Survey on State-of-the-Art Drowsiness Detection Techniques,” IEEE Access, vol. 7, pp. 61904–61919, 2019, doi: 10.1109/ACCESS.2019.2914373.
I. Stancin, M. Cifrek, and A. Jovic, “A review of eeg signal features and their application in driver drowsiness detection systems,” Sensors, vol. 21, no. 11, p. 29, 2021, doi: 10.3390/s21113786.
A. Joly, R. Zheng, T. Kaizuka, and K. Nakano, “Effect of drowsiness on mechanical arm admittance and driving performances,” IET Intell. Transp. Syst., vol. 12, no. 3, pp. 220–226, 2018, doi: 10.1049/iet-its.2016.0249.
L. Salvati, M. D’amore, A. Fiorentino, A. Pellegrino, P. Sena, and F. Villecco, “On-road detection of driver fatigue and drowsiness during medium-distance journeys,” Entropy, vol. 23, no. 2, pp. 1–12, 2021, doi: 10.3390/e23020135.
M. Erins and Z. Markovics, “Unordinary Situation Assessment of Human Fatigue and Drowsiness in Expert Decision-Making Systems,” Eng. Rural Dev., vol. 21, pp. 325–331, 2022, doi: 10.22616/ERDev.2022.21.TF101.
U. Budak, V. Bajaj, Y. Akbulut, O. Atila, and A. Sengur, “An effective hybrid model for EEG-based drowsiness detection,” IEEE Sens. J., vol. 19, no. 17, pp. 7624–7631, 2019, doi: 10.1109/JSEN.2019.2917850.
V. Noreika, S. Georgieva, S. Wass, and V. Leong, “14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants,” Infant Behav. Dev., vol. 58, no. November 2019, p. 101393, 2020, doi: 10.1016/j.infbeh.2019.101393.
Z. Liu, Y. Peng, and W. Hu, “Driver fatigue detection based on deeply-learned facial expression representation,” J. Vis. Commun. Image Represent., vol. 71, p. 102723, 2020, doi: 10.1016/j.jvcir.2019.102723.
M. Dua, Shakshi, R. Singla, S. Raj, and A. Jangra, “Deep CNN models-based ensemble approach to driver drowsiness detection,” Neural Comput. Appl., vol. 33, no. 8, pp. 3155–3168, 2021, doi: 10.1007/s00521-020-05209-7.
E. Magán, M. P. Sesmero, J. M. Alonso-Weber, and A. Sanchis, “Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images,” Appl. Sci., vol. 12, no. 3, 2022, doi: 10.3390/app12031145.
H. Z. Ilmadina, M. Naufal, and D. S. Wibowo, “Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 22, no. 3, pp. 419–430, 2023, doi: 10.30812/matrik.v22i3.2785.
I. H. Kao and C. Y. Chan, “Comparison of Eye and Face Features on Drowsiness Analysis,” Sensors, vol. 22, no. 17, 2022, doi: 10.3390/s22176529.
I. Jahan et al., “4D: A Real-Time Driver Drowsiness Detector Using Deep Learning,” Electron., vol. 12, no. 1, pp. 1–17, 2023, doi: 10.3390/electronics12010235.
M. U. Farooq, S. Mujeeb, H. Ali, A. Basit, M. Abdulrauf, and K. Siddhu, “HUB-DDNET : A Novel Driver Drowsiness Detection Method for Real-World Applications,” no. September, 2023, doi: 10.13140/RG.2.2.31225.24160.
A. Singh, H. Herunde, and F. Furtado, “Modified Haar-Cascade Model for Face Detection Issues,” Int. J. Res. Ind. Eng. , vol. 9, no. 2, pp. 143–171, 2020, doi: 10.22105/riej.2020.226857.1129.
J. L. Lisani and S. Ramis, “A contrario detection of faces with a short cascade of classifiers,” Image Process. Line, vol. 9, pp. 269–290, 2019, doi: 10.5201/ipol.2019.272.
G. Zhao, S. Zou, and H. Wu, “Improved Algorithm for Face Mask Detection Based on YOLO-v4,” Int. J. Comput. Intell. Syst., vol. 16, no. 1, 2023, doi: 10.1007/s44196-023-00286-7.
M. N. S. Harsha, “Drowsiness Detection Using Haar and CNN Algorithm,” Int. J. Food Nutr. Sci., vol. 11, no. 12, pp. 1765–1775, 2023, doi: 10.48047/ijfans/v11/i12/187.
Y. Yao, X. Zhao, H. Du, Y. Zhang, G. Zhang, and J. Rong, “Classification of fatigued and drunk driving based on decision tree methods: A simulator study,” Int. J. Environ. Res. Public Health, vol. 16, no. 11, 2019, doi: 10.3390/ijerph16111935.
I. Nasri, M. Karrouchi, H. Snoussi, K. Kassmi, and A. Messaoudi, “Detection and Prediction of Driver Drowsiness for the Prevention of Road Accidents Using Deep Neural Networks Techniques,” in WITS 2020, S. Bennani, Y. Lakhrissi, G. Khaissidi, A. Mansouri, and Y. Khamlichi, Eds., Singapore: Springer Singapore, 2022, pp. 57–64.
R. Raj et al., “Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm,” Image Vis. Comput., vol. 101, p. 103957, 2020, doi: 10.1016/j.imavis.2020.103957.
Gowsikraja, Thevakumaresh, and Raveena, “OBJECT DETECTION USING HAAR CASCADE MACHINE LEARNING ALGORITHM,” 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:250331313
F. M. Javed Mehedi Shamrat, A. Majumder, P. R. Antu, S. K. Barmon, I. Nowrin, and R. Ranjan, “Human Face Recognition Applying Haar Cascade Classifier,” in Pervasive Computing and Social Networking, G. Ranganathan, R. Bestak, R. Palanisamy, and Á. Rocha, Eds., Singapore: Springer Nature Singapore, 2022, pp. 143–157.
A. Jain, Aishwarya, and G. Garg, “Gun detection with model and type recognition using haar cascade classifier,” Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 419–423, 2020, doi: 10.1109/ICSSIT48917.2020.9214211.
W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: a real-time face detector,” Vis. Comput., vol. 37, no. 4, pp. 805–813, 2021, doi: 10.1007/s00371-020-01831-7.
A. Ismail, M. Elpeltagy, M. Zaki, and K. A. El-Dahshan, “Deepfake video detection: YOLO-Face convolution recurrent approach,” PeerJ Comput. Sci., vol. 7, pp. 1–19, 2021, doi: 10.7717/PEERJ-CS.730.
Z. Yu, H. Huang, W. Chen, Y. Su, Y. Liu, and X. Wang, “YOLO-FaceV2: A Scale and Occlusion Aware Face Detector,” pp. 1–18, 2022, [Online]. Available: http://arxiv.org/abs/2208.02019
J. Liang, Z. Qin, S. Xiao, L. Ou, and X. Lin, “Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services,” IEEE Trans. Dependable Secur. Comput., vol. 18, no. 4, pp. 1632–1644, 2021, doi: 10.1109/TDSC.2019.2922958.
B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165.
M. Shaheen, T. Zafar, and S. Ali Khan, “Decision tree classification: Ranking journals using IGIDI,” J. Inf. Sci., vol. 46, no. 3, pp. 325–339, 2020, doi: 10.1177/0165551519837176.
S. Aning and M. Przybyla-Kasperek, “Comparative Study of Twoing and Entropy Criterion for Decision Tree Classification of Dispersed Data,” Procedia Comput. Sci., vol. 207, no. Kes, pp. 2434–2443, 2022, doi: 10.1016/j.procs.2022.09.301.
Y. Miao, J. Wang, B. Zhang, and H. Li, “Practical framework of Gini index in the application of machinery fault feature extraction,” Mech. Syst. Signal Process., vol. 165, no. July 2021, p. 108333, 2022, doi: 10.1016/j.ymssp.2021.108333.
S. Tangirala, “Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, pp. 612–619, 2020, doi: 10.14569/ijacsa.2020.0110277.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
