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Volume - 6 | Issue - 1 | march 2024

AI based Identification of Students Dress Code in Schools and Universities Open Access
D. Vaduganathan  , T. Annapurna, U. Aswitha, D. Brindha  465
Pages: 1-11
Cite this article
Vaduganathan, D., T. Annapurna, U. Aswitha, and D. Brindha. "AI based Identification of Students Dress Code in Schools and Universities." Journal of Information Technology and Digital World 6, no. 1 (2024): 1-11
Published
12 February, 2024
Abstract

Automatic dress code verification (ADCV) uses advanced technology to quickly and fairly check if people are following the dress code. It is more accurate than humans and does not have personal biases. This can be helpful in places like schools, universities, and offices where dress codes are important for safety. One of the great things about it is that it saves time and money. It does not require people to manually check the dress codes, which can be slow and not always consistent. This technology can be adjusted to fit different dress code rules, whether it's a formal office dress or a school uniform. The technology can be customized for different dress code rules, from formal office attire to school uniforms. One notable advantage of ADCV is its capacity to save both time and financial resources. The system gets rid of the requirement for people to manually check dress codes, which can be slow and inconsistent. The technology's flexibility extends to customization for different dress code rules, addressing the unique needs of diverse settings. It is important to highlight the evolution of ADCV from previous models, where limitations existed in terms of singular application focus. In earlier iterations, functionalities such as face recognition or object detection could only be implemented individually, creating a gap in the comprehensive identification of individuals that encompassed both facial features and attire. The proposed system aims to rectify these shortcomings by integrating capabilities, ensuring a more holistic approach to dress code verification. This enhancement signifies a significant stride toward a more versatile and effective solution for monitoring and enforcing dress codes in various environments. In the previous models the only one application could be designed. For instance, the face recognition or the object detection is only possible individually. It cannot do both identification of person’s face and attire of the person. Considering these disadvantages, the rectification of these problems will be achieved through the implementation of the proposed system. ADCV's impact extends beyond its immediate application in dress code enforcement. As a pioneering example of the intersection between computer vision and artificial intelligence.

Keywords

Face Recognition Object Detection Deep Learning Computer Vision

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