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Facial length and angle feature recognition for digital libraries.

Shuangyan Li1, Min Ji2, Ming Chen2

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This summary is machine-generated.

This study introduces a new facial recognition method using length and angle features for digital libraries. The attention-based approach achieves high accuracy in recognizing various facial expressions, enhancing biometric technology.

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Facial recognition is a mature biometric technology, but accuracy remains a challenge.
  • Digital libraries require robust facial feature recognition for improved services.

Purpose of the Study:

  • To propose a novel facial recognition method using length and angle features for digital libraries.
  • To enhance the accuracy and stability of facial expression recognition using attention mechanisms.

Main Methods:

  • Developed a facial action network architecture incorporating attention mechanisms.
  • Explored a network architecture focusing on facial expression length and angle features.
  • Constructed an end-to-end framework utilizing attention for facial feature points.

Main Results:

  • Achieved an average recognition rate of 97.28%–99.97% for seven common expressions on the FER-2013 dataset.
  • Reported the highest recognition rates for happiness and surprise (99.97%) and lower rates for anger, fear, and neutrality (97.18%).
  • Demonstrated high accuracy and robustness in facial expression recognition, especially in complex environments.

Conclusions:

  • The proposed attention-based method significantly improves facial expression recognition accuracy and stability.
  • The method offers reliable technical support for digital libraries and other applications.
  • This research promotes advancements in facial recognition technology for digital library services and user experience.