Guest Editorial: The Computational Face

The papers in this special section examine the concept of automated face analysis (AFA). AFA has received special attention from the computer vision and pattern recognition communities. Research progress often gives the impression that problems such as face recognition and face detection are solved, at least for some scenarios. Several aspects of face analysis remain open problems, including the implementation of large scale face recognition/detection methods for in the wild images, emotion recognition, micro-expression analysis, and others. The community keeps making rapid progress on these topics, with continual improvement of current methods and creation of new ones that push the state-of-the-art. Applications are countless, including security and video surveillance, human computer/robot interaction, communication, entertainment, and commerce, while having an important social impact in assistive technologies for education and health. The importance of face analysis, together with the vast amount of work on the subject and the latest developments in the field, motivated us to organize a special section on this theme. The scope of the compilation comprises all aspects of face analysis from a computer vision perspective. Including, but not limited to: recognition, detection, alignment, reconstruction of faces, pose estimation of faces, gaze analysis, age, emotion, gender, and facial attributes estimation, and applications among others.

[1]  Jiwen Lu,et al.  Two-Stream Transformer Networks for Video-Based Face Alignment , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Patrick Snape,et al.  Disentangling the Modes of Variation in Unlabelled Data , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ming Shao,et al.  Visual Kinship Recognition of Families in the Wild , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Sergio Escalera,et al.  ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[5]  Nicu Sebe,et al.  Recurrent Convolutional Shape Regression , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jean-Marc Odobez,et al.  HeadFusion: 360° Head Pose Tracking Combining 3D Morphable Model and 3D Reconstruction , 2018, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Sergio Escalera,et al.  ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[8]  Victor Lempitsky,et al.  Photorealistic Monocular Gaze Redirection Using Machine Learning , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Lijun Yin,et al.  EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Sergio Escalera,et al.  ChaLearn looking at people: A review of events and resources , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).