A Lightweight and Accurate Face Detection Algorithm Based on Retinaface
This is an incremental improvement for efficient face detection in resource-constrained applications.
The paper tackles face detection by proposing LAFD, a lightweight algorithm based on RetinaFace, achieving average accuracies of 94.1%, 92.2%, and 82.1% on WIDERFACE easy, medium, and hard subsets, with improvements of up to 8.3% over RetinaFace and 4.1% over LFFD, while maintaining a model size of 10.2MB.
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.