Details

Autor(en) / Beteiligte

• Xia, Jiahao
• Zhang, Haimin
• Wen, Shiping
• Yang, Shuo
• Xu, Min

Titel

An efficient multitask neural network for face alignment, head pose estimation and face tracking

Ist Teil von

• Expert systems with applications, 2022-11, Vol.205, p.117368, Article 117368

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• While Convolutional Neural Networks (CNNs) have significantly boosted the performance of face related algorithms, maintaining accuracy and efficiency simultaneously in practical use remains challenging. The state-of-the-art methods employ deeper networks for better performance, which makes it less practical for mobile applications because of more parameters and higher computational complexity. Therefore, we propose an efficient multitask neural network, Alignment & Tracking & Pose Network (ATPN) for face alignment, face tracking and head pose estimation. Specifically, to achieve better performance with fewer layers for face alignment, we introduce a shortcut connection between shallow-layer and deep-layer features. We find the shallow-layer features are highly correspond to facial boundaries that can provide the structural information of face and it is crucial for face alignment. Moreover, we generate a cheap heatmap based on the face alignment result and fuse it with features to improve the performance of the other two tasks. Based on the heatmap, the network can utilize both geometric information of landmarks and appearance information for head pose estimation. The heatmap also provides attention clues for face tracking. The face tracking task also saves us the face detection procedure for each frame, which also significantly boost the real-time capability for video-based tasks. We experimentally validate ATPN on four benchmark datasets, WFLW, 300VW, WIDER Face and 300W-LP. The experimental results demonstrate that it achieves better performance with much less parameters and lower computational complexity compared to other light models. •Employ the structural information at shallow layer for efficient face alignment.•Generate a heatmap to provide the geometric and attention information for network.•Propose an efficient multitask framework for video-based processing.•Conduct comparison experiments and ablation studies on various datasets.