Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning
Article Sidebar
Main Article Content
Abstract
This paper explores the utilization of deep learning techniques for the detection and classification of dress code violations in educational environments, identifying the challenges of manual enforcement and the potential for systems that are automated. This paper exhibits a model that integrates Faster R-CNN for detection and EfficientNet for classification, which provides an accurate and very efficient system to monitor students’ compliance with the dress code policies. The model was trained on a dataset of images that were collected from Federal University Dutsin-Ma and were classified into “decent” and “indecent” dressing for both male and female students. The result achieved demonstrates that the model works efficiently, reaching a training accuracy of 98% and a validation accuracy of 96%, and with overall scores for precision, recall, and F1-score exceeding 97%, thereby proving its effectiveness in different dress code categories. The Uniformity across the techniques substantiates the feature extraction performance of the model and demonstrates its generalization ability. This paper outlines the benefits of automation in alleviating bias and human error by improving transparency and fairness and enforcing the dress code. The results showed how it is effective by combining powerful deep learning models with strong frameworks to solve problems of classification.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
DeBiasse, M. A., Peters, S. M., & Bujiriri, B. (2022). Dress codes written for dietetics education programs: a foucauldian discourse analysis. Feminism & Psychology, 33(2), 276-294. https://doi.org/10.1177/09593535221126797
Ruzycki, S. M., Daodu, O., Hernandez, S., & Lithgow, K. (2022). The contribution of undergraduate medical education dress codes to systemic discrimination: a critical policy analysis. Medical Education, 56(9), 949-957. https://doi.org/10.1111/medu.14854
Knipp, H. & Stevenson, R. (2021). “A powerful visual statement”: race, class, and gender in uniform and dress code policies in new orleans public charter schools. Affilia, 37(1), 79-96. https://doi.org/10.1177/08861099211010026
Shen, X. & Yuan, C. (2021). A college student behavior analysis and management method based on machine learning technology. Wireless Communications and Mobile Computing, 2021(1). https://doi.org/10.1155/2021/3126347
Suleiman, A. B., Luka, S., & Ibrahim, M. (2023). Cardiovascular disease prediction using random forest machine learning algorithm. Fudma Journal of Sciences, 7(6), 282-289.
Zhang, X., Wang, N., Shen, H., Ji, S., Luo, X., & Wang, T. (2020). Interpretable deep learning under fire. In 29th {USENIX} security symposium ({USENIX} security 20).
Fakunle, A. O., Fawole, A. O., Ayodeji, B. O., & Ayeni, O. M. (2024). Real-Time Dress Code Adherence Recognition in an Academic Setting Using a Deep Learning Model. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 12(7), 1268-1276. https://doi.org/10.22214/ijraset.2024.63641
Abubakar, M., Surajo, Y., & Tasiu, S. (2025). An Explainable Deep Learning Model for Illegal Dress Code Detection and Classification. Journal of Basics and Applied Sciences Research (JOBASR), 3(1), 1-10. https://doi.org/10.4314/jobasr.v3i1.1
Durgam, L. K., & Jatoth, R. K. (2023). Real-Time Dress Code Detection using MobileNetV2 Transfer Learning on NVIDIA Jetson Nano. Proceedings of the 11th International Conference on Information Technology: IoT and Smart City (ICIT 2023), Kyoto, Japan, 8. https://doi.org/10.1145/3638985.3638987
Bedeli, M., Geradts, Z., & van Eijk, E. (2018). Clothing identification via deep learning: Forensic applications. Forensic Science Research, 3(3), 219-229.https://doi.org/10.1080/20961790.2018.1526251
Kowshik, P. B., Sundar, P. S., Krishna, A. V., & Reddy, P. (2020). Classification of Dress Codes Using Convolution Neural Networks. Proceedings of the Second International Conference on Inventive Research in Computing Applications (ICIRCA 2020), IEEE, 314-319.
Agarwal, Divyangini & Gupta, Prashant & Eapen, Naived. (2023). A Framework for Dress Code Monitoring System using Transfer Learning from Pre-Trained YOLOv4 Model. 1-5. 10.1109/ICETET-SIP58143.2023.10151460.
Azizan, M. A., & Zaini, N. (2023, July). Video Analysis to Detect Dress Code Violations in Laboratories. In 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA) (1-6). IEEE.
Rebekah, J., Wise, D. J. W., Bhavani, D., Regina, P. A., & Muthukumaran, N. (2020, July). Dress code surveillance using deep learning. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (394-397). IEEE.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48.
Zhang, Q., Chang, X., Meng, Z., & Li, Y. (2021). Equipment detection and recognition in electric power room based on faster R-CNN. Procedia Computer Science, 183, 324-330.
Sani, Z., Prasad, R., & Hashim, E. K. (2024). Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images. Evolving Systems, 15(1), 25-40.
Bashir, A. S., Luka, S., & Nda Ndabula, J. (2025). A CNN-SVM Based Model for Detection and Classification of Tomato Leaf Diseases. Journal of Basics and Applied Sciences Research, 3(3), 8-15. https://dx.doi.org/10.4314/jobasr.v3i3.2.
Hou, Y., Zhang, H., Zhou, S., & Zou, H. (2017). Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark‐Based Visual Localization of Autonomous Vehicles. Mobile information systems, 2017(1), 8104386.
Koonce, B. (2021). EfficientNet. In Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization , 109-123