LFLD: A Lightweight Facial Landmark Detector Based on Auxiliary Heatmap

In recent years, heatmap regression models have attracted much attention due to their superior performance in locating facial landmarks. These models overcome the lack of spatial and contextual information of coordinate regression models. However, there are still two major problems with these models; (1) they are computationally expensive, (2) they have the quantization error. To address two issues, we present a lightweight facial landmark detector named LFLD that unifies ShuffleNetV2 with the principles of the high-resolution structure for facial landmark detection. Our LFLD has high accuracy and significantly reduced computation costs compared to other heatmap regression models. In addition, the LFLD is equipped with two detection heads that generate the primary heatmaps and the auxiliary heatmaps. The primary heatmaps represent the integer part of the predicted facial landmarks coordinates, and the auxiliary heatmaps indicate the fractional part. Our LFLD uses these two heatmaps to jointly represent the predicted facial landmarks coordinates. The extensive experimental results show that LFLD can achieve satisfactory results in the case of low parameters and FLOPs. Particularly for the NME, our LFLD reaches 4.31% on WFLW and 3.54% on 300W. In heatmap regression, when compared to HRNet, our LFLD has lower NME than HRNet and has 45% fewer parameters and 66% fewer FLOPs. Finally, our model has fewer parameters than large networks, and the detection accuracy is comparable to large networks.