Deep Recurrent Regression with a Heatmap Coupling Module for Facial Landmarks Detection

Facial landmarks detection is an essential step in many face analysis applications for ambient understanding (people, scenes) and for dynamically adapting the interaction with humans and environment. The current methods have difficulties with real-world images. This paper proposes a simple and effective method to detect the essential points in human faces. The proposed method comprises a two-stage coordinated regression deep convolutional neural network (CR-CNN) with a heatmap coupling module to convert the detected facial landmarks of the first stage into a Gaussian heatmap. To take advantage of the prior stage knowledge, the generated heatmap is concatenated with the original image of the input face and entered into the network in the second stage. The two-stage implementation based on CR-CNN has same layers structure to simplify the design and complexity. The L₁ loss function is used for each stage and the total loss equals the sum of the two loss functions from both stages. Comprehensive experiments are conducted to evaluate the proposed method on three common challenging facial landmark datasets, namely AFLW, 300W, and WFLW. The proposed method achieves normalized mean error (NME) of 1.56% on the AFLW, 4.20% on the 300W, and 5.53% on the WFLW datasets. Moreover, the execution time of the proposed two-stage CR-HC is calculated as 3.33 ms. The obtained results show the robustness and outstanding performance of the proposed method over some of the state-of-the-art methods. The source code is provided as an open repository to the community for further research activities.