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May 4, 2026World Electric Vehicle JournalOpen Access

Human Facial Keypoint Localization Based on T-Shaped Features and the Supervised Descent Method (TSDM)

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Authors

YHYi-Wen HeXHXiao-ci Huang

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Overview

Randomized trial demonstrates enhanced facial landmark localization, suggesting practical applications in driver monitoring systems.

Key Points

  • To develop a novel method for accurate facial landmark localization using T-shaped features and the Supervised Descent Method.
  • Integrate T-shaped features with the Supervised Descent Method for localization.
  • Use AdaBoost for selecting T-shaped features to enhance face detection robustness.
  • Evaluate TSDM against traditional methods and lightweight deep learning models for performance.
  • TSDM achieves a face detection rate of 97.43% with a normalized mean error of 3.4%.
  • Demonstrates higher accuracy and lower false-positive rates than traditional methods like Haar and LBPH.
  • Outperforms several lightweight deep learning models in real-time performance on CPU-only platforms.

Cite This Study

He et al. (2026) studied this question.

synapsesocial.com/papers/69f837ab3ed186a739981dfehttps://doi.org/10.3390/wevj17050237
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