STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD

STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD

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Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale.This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features.CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles.The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student.The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.

5% rumchata proof at the 150th epoch.However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications.Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale.To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience.Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization.

By caruso rhodiola implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.