Performance Trade-off of Anchor-Based and Anchor-Free Approaches of Faster R-CNN for Face Detection
DOI:
10.33395/sinkron.v10i1.15804Keywords:
Face Detection, Faster R-CNN, Anchor-free, Anchor-based, RPNAbstract
The face is a unique biometric feature that plays a crucial role in individual identification as it holds essential information for identity recognition. Face detection technology has been experiencing significant advancements in the field of computer vision. However, face detection technology continues to face challenges in balancing high detection accuracy with computational efficiency. While deep learning has advanced this field, there remains a lack of comparative studies that compare the performance trade-offs between anchor-based and anchor-free region proposal mechanisms within a Faster R-CNN framework. This research objective is comparing the performance of face detection using two approaches: anchor-based and anchor-free. The anchor-based approach use anchor boxes to predict bounding boxes, while the anchor-free approach predicts bounding boxes directly from pixel positions oriented around a point. The anchor-based approach is implemented use base line region proposed network method, whereas the anchor-free approach use a centerpoint method. The study utilizes a custom dataset comprising 1,000 formal images of students from Del Institute of Technology, split into 900 training images and 100 testing images. Performance evaluation is conducted based on metrics such as intersection over union, precision, recall, and latency. The results demonstrate that the anchor-based approach achieves superior accuracy with an average IoU of 0.98 but requires a longer detection time of approximately 2.33 seconds per image. Conversely, the anchor-free approach offers significantly faster processing at 0.14 seconds per detection, though with a lower average IoU of 0.78. This study concludes that while anchor-based methods excel in precision, anchor-free architectures provide alternative for time-critical applications, offering a clear reference for optimizing future face detection systems.
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Copyright (c) 2026 Arie Satia Dharma, Herimanto, Niar Fujita Simbolon, Anton Roycar Nababan
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Muhammad Khoiruddin harahap
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