A Lightweight and Accurate Face Detection Algorithm Based on Retinaface
Baozhu Liu Hewei Yu
In this paper, we propose a lightweight and accurate face detection algorithm
LAFD (Light and accurate face detection) based on Retinaface. Backbone network
in the algorithm is a modified MobileNetV3 network which adjusts the size of
the convolution kernel, the channel expansion multiplier of the inverted
residuals block and the use of the SE attention mechanism. Deformable
convolution network(DCN) is introduced in the context module and the algorithm
uses focal loss function instead of cross-entropy loss function as the
classification loss function of the model. The test results on the WIDERFACE
dataset indicate that the average accuracy of LAFD is 94.1%, 92.2% and 82.1%
for the "easy", "medium" and "hard" validation subsets respectively with an
improvement of 3.4%, 4.0% and 8.3% compared to Retinaface and 3.1%, 4.1% and
4.1% higher than the well-performing lightweight model, LFFD. If the input
image is pre-processed and scaled to 1560px in length or 1200px in width, the
model achieves an average accuracy of 86.2% on the 'hard' validation subset.
The model is lightweight, with a size of only 10.2MB.