DEEP GEOMETRY EMBEDDING NETWORKS FOR ROBUST FACIAL LANDMARK DETECTION

Facial landmark detection has witnessed substantial progress due to introducing convolutional neural networks. Nonetheless, current convolutional neural networks-based approaches ignore the useful geometric relationship between different facial locations. To address this issue, we propose a new module to model the facial geometric relationship. The module can be integrated into the convolutional neural networks architecture to obtain the geometric representation, whereafter we leverage bilinear pooling operation to embed it into high-level feature maps of original face image so as to produce the more discriminative face representation. Extensive evaluation experiments on multiple challenging benchmark datasets demonstrate that our captured geometric information is robust against occlusion and head pose variation and our proposed method outperforms state-of-the-art methods.