A Robust Facial Landmark Detector with Mixed Loss

Abstract

Facial landmark detection is one of the most important tasks in face image and video analysis. Existing algorithms based on deep convolutional neural networks have achieved good performance in public benchmarks and practical applications such as face verification, expression analysis, beauty applications and so on. However, the performance of a facial landmark detector degrades significantly when dealing with challenging facial images in the presence of extreme appearance variations such as pose, expression, occlusion, etc. To mitigate these difficulties, we propose a robust facial landmark detection algorithm based on coordinates regression in an end-to-end training fashion. By using the soft-argmax function, the network weights can be optimised with a mixed loss function. The online pose-based data augmentation technology is used to effectively solve the data imbalance problem and improve the robustness of the proposed method. Experiments conducted on the 300-W and AFLW datasets demonstrate that the performance of the proposed algorithm is competitive to the state-of-the-art heatmap regression algorithms, in terms of accuracy. Besides, our method achieves real-time speed on 300-W with 68 landmarks, which runs at 85 FPS on a Tesla v100 GPU.