Abstract
Face detection in resource-constrained environments presents challenges, due to the computational demands of state-of-the-art models and the complexity of real-world conditions, such as variations in scale, pose, and occlusion. This study introduces FeatherFace, a lightweight face-detection architecture with only 0.49 M parameters, designed for high accuracy and efficiency in such environments. Leveraging MobileNet-0.25 as a backbone, FeatherFace incorporates advanced feature-integration strategies, including a bidirectional feature pyramid network (BiFPN), a convolutional block attention module (CBAM), deformable convolutions, and channel shuffling. Evaluated on the WIDERFace dataset, FeatherFace achieves an overall average precision (AP) of 87.2%, with notable performance gains of 4.0% AP on the Hard subset compared with the baseline. Ablation studies highlight the critical role of multiscale feature integration and the strategic placement of attention mechanisms in addressing detection challenges such as small or occluded faces. With its compact design and reduced inference time, FeatherFace bridges the gap between the reliability of computationally intensive models and the need for deploying robust models in highly resource-constrained environments, such as edge devices and embedded systems. This work provides valuable insights for developing robust and lightweight models suited to challenging real-world applications.
Published Version
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