Traditional Solution to Identify College Uniform Using CNN: Review

Traditional Solution to Identify College Uniform Using CNN: Review

Md. Raufik Thekiya Assistant professor

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India r.rhekiya.jit@gmail.com

Bhushan Rajani Assistant Proffesor

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India gunjarninawe2808@gmail.com

Yash Mahadik

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India mahadiky442@gmail.com

Gunjar Ninawe

Department of Computer science and Engineering

Jhulelal Institute of Technology Nagpur , India Gunjarninawe2808@gmail.com

Ritesh Shivankar

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India riteshshivankar8@gmail.com

Vaibhav Dhone

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India vaibhavdhone0007@gmail.com

Sujal Gajbhiye

Department of Computer Science and Engineering

Jhulelal Institute of Technology Nagpur, India Sujalgajbhiye3@gmail.com

Abstract — The purpose of this paper is to develop an AI-managed system that automatically automatically enforce the enforcement of university dress code policies using a tool, teachable machine, a device for training machine learning models. The system uses camera-based technology to monitor students as they enter the premises, ensure compliance with the dress code of the institute without the need for manual supervision. AI analyzes students' dress in real time, recognizing whether the required items such as formal shirts, tie, jackets or ID cards are present. If a violation is detected, the system immediately sends a notification to the student, which specifies the missing items (s), as well as a photo of the student for accurate time and record-keeping. By automating the process, the system reduces the capacity  for  human  error,  prejudice  and

inconsistent enforcement that is often accompanied by manual checks. This ensures fairness and fairness in the monitoring process, as AI depends only on visual data to assess compliance. Additionally, the system continuously operates, providing 24/7 surveillance without the requirement of brakes or supervision shifts. The AI-manufactured approach also increases efficiency by reducing the required time and effort from employees to manually check each student's dress. Real -time notifications enable students to fix any violations immediately, reduce the classroom disruption and improve the overall learning environment. With the required minimum human intervention after the setup, this automated system ensures a transparent, consistent and reliable method to maintain dress code compliance to benefit both students and university employees.