“LFFD: A Light and Fast Face Detector for Edge Devices”, Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, Chunhong Pan2019-04-24 (Danbooru AI; backlinks; similar)⁠:

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 these 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 8 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 640×480; NVIDIA TX2: 136.99 PFS at 160×120; Raspberry Pi 3 Model B+: 8.44 FPS at 160×120) with model size of 9 MB.

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