Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In...

Area of Science:

• Computer Science
• Artificial Intelligence

Background:

• Traditional shallow image features are insufficient for modern data demands.
• Deep learning networks offer advanced capabilities for image representation and analysis.
• Face recognition technology has matured with the integration of deep learning algorithms.

Purpose of the Study:

• To apply deep learning face image evaluation algorithms on mobile terminals for student check-in management.
• To develop and evaluate an efficient face detection model for student attendance.
• To improve the accuracy and speed of face recognition in educational settings.

Main Methods:

• A face image detection model using a deep learning network for face detection.
• A face detection algorithm combining candidate region and deep learning networks.
• A cascaded convolution network for face key point detection.
• Optimization of loss functions and extraction of face binary features for efficiency.

Main Results:

• The proposed deep learning network improves face detection and classification accuracy.
• Retrieval time for face image analysis is significantly reduced.
• The algorithm demonstrates enhanced performance in face image evaluation for student management.

Conclusions:

• Deep learning-based face detection offers a more efficient and accurate solution for student check-in management.
• The developed methods improve the overall performance of face recognition systems on mobile terminals.
• This approach provides a robust framework for leveraging AI in educational administration.