A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling

By Darren P. Cosker, Eva G. Krumhuber, Adrian Hilton. 2011

182 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

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Citations in the corpus

Generating 3D faces using Convolutional Mesh Autoencoders (2018, background): “Existing large scale datasets [14, 16, 50, 49, 38] do not contain high resolution extreme facial expressions.”
Automatic Analysis of Facial Actions: A Survey (2019, unknown): “AM-FED [117] Induced N/A Various head poses D 10 N N Bosphorous [155] On command 105 subjects 3D multi-pose S 25 N Y BP4D [218] Induced 41 subjects 3D multi-pose D 27 N Y CASME [207] Induced (micro) 35 subjects Near frontal D F Y N CASME II [206] Induced (micro) 26 subjects Near frontal D F Y N Cohn”
Capture, Learning, and Synthesis of 3D Speaking Styles (2019, background): “3D face datasets: Several 3D face datasets have been released that focus on the analysis of static 3D facial shape and expression (e.g. [13, 47, 65]) or dynamic facial expressions (e.g. [2, 15, 18, 44, 64, 68, 69]).”
Multilinear Autoencoder for 3D Face Model Learning (2018, methodology): “Acknowledgements We would like to thank Timo Bolkart for providing the registrations for D3DFACS data [18], and Mengjiao Wang for providing the code of [27].”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, methodology): “Examples of commercial systems available that employmulti-view stereo techniques are theDI3D (Dimensional Imaging [49]) dynamic face capturing system, and the 3DMD dynamic 3D stereo system [50], each of which have been used for 3D database acquisition ([51] and [52,53] respectively).”
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis (2016, background): “Although there are several facial expression databases that include 3D data (e.g., BU-3DFE [33], BU-4DFE [32], Bosphorus [21], ITC-3DRFE [25], ETH-3DAV [9], and 3D AU-DB [5]), they are all based on posed behavior and typically include few subjects, little diversity, limited ground truth labels, and ”
Learning a model of facial shape and expression from 4D scans (2017, methodology): “The expressionmodel, E, uses two sources of training data, namely registrations of D3DFACS [Cosker et al. 2011] and self-captured sequences.”
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction (2020, methodology): “To obtain the accurate 3D face shape, a number of works reconstructed the 3D face using active method including depth sensor or scanner[56, 55, 3, 40, 39, 12, 17], while the other works built sparse multi-view camera system[57, 18].”
A 3D Morphable Model Learnt from 10,000 Faces (2016, methodology): “This approach is similar to the work of [16] which was shown to be successful for temporal person-specific sequences, but here we pay particular attention to mesh sets with highly variable identity.”
Large Scale 3D Morphable Models (2017, methodology): “Cosker et al. (2011) automated the procedure of landmark annotations required for the TPS warp, for the special case of temporal sequences of a single identity displaying emotions.”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, methodology): “The CMU Multi-PIE database [34], 3Ddynamic AU database [35], Bosphorus database [9], KDEF [33], BU 3D facial expression databases [14,15], and ICT-3DRFE database [24] begin to address the need for 3D (or multi-view) data but are limited to posed facial behavior.”
3D Morphable Face Models—Past, Present, and Future (2019, unknown): “D3DFACS [Cosker et al. 2011] triangle mesh (30k vertices), 1,024 × 1,280 UV texture map face, neck, some-”