LFFD: A Light and Fast Face Detector for Edge Devices

TL;DR

This paper presents LFFD, a lightweight, anchor-free, one-stage face detector optimized for edge devices, achieving high accuracy and speed with a novel understanding of receptive fields and efficient backbone design.

Contribution

The paper introduces a novel face detection method that leverages receptive field anchors and an efficient backbone, enabling fast, accurate detection on resource-limited edge devices.

Findings

Achieves high accuracy on WIDER FACE and FDDB benchmarks.

Runs at over 130 FPS on high-end GPUs and 8 FPS on Raspberry Pi.

Model size is only 9 MB, suitable for edge deployment.

Abstract

Face detection, as a fundamental technology for various applications, is always deployed on edge devices which have limited memory storage and low computing power. This paper introduces a Light and Fast Face Detector (LFFD) for edge devices. The proposed method is anchor-free and belongs to the one-stage category. Specifically, we rethink the importance of receptive field (RF) and effective receptive field (ERF) in the background of face detection. Essentially, the RFs of neurons in a certain layer are distributed regularly in the input image and theses RFs are natural "anchors". Combining RF "anchors" and appropriate RF strides, the proposed method can detect a large range of continuous face scales with 100% coverage in theory. The insightful understanding of relations between ERF and face scales motivates an efficient backbone for one-stage detection. The backbone is characterized by eight detection branches and common layers, resulting in efficient computation. Comprehensive and extensive experiments on popular benchmarks: WIDER FACE and FDDB are conducted. A new evaluation schema is proposed for application-oriented scenarios. Under the new schema, the proposed method can achieve superior accuracy (WIDER FACE Val/Test -- Easy: 0.910/0.896, Medium: 0.881/0.865, Hard: 0.780/0.770; FDDB -- discontinuous: 0.973, continuous: 0.724). Multiple hardware platforms are introduced to evaluate the running efficiency. The proposed method can obtain fast inference speed (NVIDIA TITAN Xp: 131.45 FPS at 640x480; NVIDIA TX2: 136.99 PFS at 160x120; Raspberry Pi 3 Model B+: 8.44 FPS at 160x120) with model size of 9 MB.