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Facial detection has become essential in fields like security and surveillance, requiring efficient and accurate systems. This study evaluates and compares six frameworks—RetinaFace, Mediapipe, YuNet, YoloFace, Haarcascade, and MTCNN—using the WIDER FACE dataset of 3,226 images. Performance is assessed on an Intel Core i5 13th generation 13600K CPU with 32 GB RAM, focusing on faces recognized, processing time, and average detection time per face. RetinaFace demonstrated the highest detection capability with 22,738 faces but required significant processing time (23,153.78 s, 3.83 s/face). In contrast, YuNet was the most efficient, averaging 0.03 s per face, followed by Mediapipe at 0.04 s, although with fewer detections. MTCNN and YoloFace showed intermediate performance in detection and efficiency. This study highlights key differences in deep learning-based facial detection frameworks, offering insights for future research and applications in the field.

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Benchmark: Face Detection Using Deep Learning Models and Frameworks

  • 会议论文

  • 发表日期：

    2025年

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  • E. Jiménez-Delgado,
  • A. Méndez-Porras,
  • Sixto Enrique Campaña Bastidas,
  • L. Calvo-Valverde,
  • J. Alfaro-Velasco,
  • J. Rodriguez-Salas

摘要

Facial detection has become essential in fields like security and surveillance, requiring efficient and accurate systems. This study evaluates and compares six frameworks—RetinaFace, Mediapipe, YuNet, YoloFace, Haarcascade, and MTCNN—using the WIDER FACE dataset of 3,226 images. Performance is assessed on an Intel Core i5 13th generation 13600K CPU with 32 GB RAM, focusing on faces recognized, processing time, and average detection time per face. RetinaFace demonstrated the highest detection capability with 22,738 faces but required significant processing time (23,153.78 s, 3.83 s/face). In contrast, YuNet was the most efficient, averaging 0.03 s per face, followed by Mediapipe at 0.04 s, although with fewer detections. MTCNN and YoloFace showed intermediate performance in detection and efficiency. This study highlights key differences in deep learning-based facial detection frameworks, offering insights for future research and applications in the field.