Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep feature fusion of facial parts. In particular, each textured 3D face scan is firstly represented as a 2D texture map and a depth map with one-to-one dense correspondence. Then, the facial parts of both texture map and depth map are extracted using a novel 4-stage process consists of facial landmark localization, facial rotation correction, facial resizing, facial parts bounding box extraction and post-processing procedures. Finally, deep fusion Convolutional Neural Networks (CNNs) features of all facial parts are learned from both texture maps and depth maps, respectively and nonlinear SVMs are used for expression prediction. Experiments are conducted on the BU-3DFE database, demonstrating the effectiveness of combing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting.

Densely tracking sequences of 3D face scans

3D face dense tracking aims to find dense inter-frame correspondences in a sequence of 3D face scans and constitutes a powerful tool for many face analysis tasks, e.g., 3D dynamic facial expression analysis. The majority of the existing methods just fit a 3D face surface or model to a 3D target surface without considering temporal information between frames. In this paper, we propose a novel method for densely tracking sequences of 3D face scans, which ex- tends the non-rigid ICP algorithm by adding a novel specific criterion for temporal information. A novel fitting framework is presented for automatically tracking a full sequence of 3D face scans. The results of experiments carried out on the BU4D-FE database are promising, showing that the proposed algorithm outperforms state-of-the-art algorithms for 3D face dense tracking.