Can #chatgpt write a #python code for Face recognition based smart attendance system? | #openai

Facial recognition is the most feasible option available in the current situation for organizations to make employee attendance and visitor entry contactless. Touchless systems are going to become the norm as organizations get their workplaces COVID ready. Businesses that were using fingerprint biometric or any touch-based systems for time, attendance, and visitor management are going to have to consider
switching to contactless systems like face recognition devices.

What Is A Facial Recognition Attendance System?
A facial recognition attendance system uses facial recognition technology to identify and verify a person using the person's facial features and automatically mark attendance. The software can be used for different groups of people such as employees, students, etc. The system records and stores the data in real-time.

What’s the hype all about?
A facial recognition attendance system is a contactless way to manage visitors and employees in an organization. Unlike other types of biometric systems, such as fingerprint that captures identity though touch, a facial recognition system is a touchless way to manage employees and visitors. In times of the COVID-19 pandemic, a touchless system is an effective preventive measure. It helps manage the inflow and outflow of people in buildings and premises in a safe and efficient manner.

ChatGPT

ChatGPT (Generative Pre-trained Transformer) is a chatbot launched by OpenAI in November 2022. It is built on top of OpenAI's GPT-3 family of large language models, and is fine-tuned (an approach to transfer learning) with both supervised and reinforcement learning techniques.
ChatGPT was launched as a prototype on November 30, 2022, and quickly garnered attention for its detailed responses and articulate answers across many domains of knowledge. Its uneven factual accuracy was identified as a significant drawback. ChatGPT was fine-tuned on top of GPT-3.5 using supervised learning as well as reinforcement learning. Both approaches used human trainers to improve the model's performance. In the case of supervised learning, the model was provided with conversations in which the trainers played both sides: the user and the AI assistant. In the reinforcement step, human trainers first ranked responses that the model had created in a previous conversation. These rankings were used to create 'reward models' that the model was further fine-tuned on using several iterations of Proximal Policy Optimization (PPO). Proximal Policy Optimization algorithms present a cost-effective benefit to trust region policy optimization algorithms; they negate many of the computationally expensive operations with faster performance.
The models were trained in collaboration with Microsoft on their Azure supercomputing infrastructure. In addition, OpenAI continues to gather data from ChatGPT users that could be used to further train and fine-tune ChatGPT. Users are allowed to upvote or downvote the responses they receive from ChatGPT; upon upvoting or downvoting, they can also fill out a text field with additional feedback.

Download the Source Code for Driver Drowsiness Detection in Python

The requirement for this Python project is pygame module which can be installed using command:
1. Cv2 – pip install opencv-python
2. Numpy - pip install numpy
3. Sqlite3 - pip install db-sqlite3
4. Sklearn - pip install scikit-learn
5. dlib - pip install dlib

Conclusion:
Employee and visitor management systems are not going to look the same again. Digital touch-based visitor management systems and fingerprint biometric systems for authentication are soon going to be replaced by touchless facial recognition technology.
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