AMFG 2017 - 7th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG)

The 7th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG)
----Embracing Face and Gesture Analysis in Social Media with Deep Learning
-AT-ICCV 2017, Oct. 28, Venice, Italy
Deep learning, especially the Convolutional Neural Networks have taken the computer vision community by storm, significantly improving the state of the art in many applications. Among them, face recognition, which has accumulated large quantities of training data in the past few decades due to the availability of online resources as well as dedicated efforts of many researchers, has been dramatically changed on the strength of the new/efficient deep learning models, and we still witness this consistent streams of record breaking improvements in many benchmarks, e.g., Labeled Faces in the Wild database (LFW), YouTube Faces database, most recently, CASIA Web-Face, MegaFace, MS-Celeb-1M, and real-world or commercial systems.
Besides the efforts putting forward to the conventional face recognition research, visual under-standing of social media content has attracted the interest of industrial and academic research communities from different domains. Visual understanding of social media includes face and body tracking (e.g., facial expression analysis, face detection, gesture recognition), facial and body characteristic analysis (e.g., gait, age, gender, and ethnicity), human behavior understanding and emotion recognition from face and gesture, social context understanding (e.g., kinship, personality, and beauty) and visual sentiment analysis. Creating effective models under visual uncertainty has significant scientific and practical values in applications of human-computer interaction, social media analytics, video indexing, visual surveillance, and Internet vision. To that end, researchers have made substantial progress, especially when off-the-shelf vision products are available, e.g. Kinect, Leap, SHORE, and Affdex. However, great challenges remain in the social media domain, especially under unconstrained imaging conditions from diverse sources with non-cooperative users. Here, we are especially interested in bringing in the cutting-edge techniques and recent advances in deep learning to solve the challenges above in social media.
This one-day serial workshop (AMFG2017) will provide a forum for researchers to review the recent progress of recognition, analysis and modeling of face, gesture, and body, and embrace the most advanced deep learning system to address face and gesture analysis particularly under unconstrained environment such as social media. The workshop will consist of one to two invited talks; one from industry, together with peer-reviewed regular papers (oral and poster). Original high-quality contributions are solicited on the following topics:
1. Deep learning methodology, theory, and its applications to social media analytics;
2. Novel deep learning model, deep learning survey, or comparative study for face/gesture recognition;
3. Deep learning for internet-scale soft biometrics and profiling: age, gender, ethnicity, personality, kinship, occupation, beauty, and fashion classification by facial and/or body descriptor;
4. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
5. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis and recognition for cross-domain social media;
6. Deep learning for detection and recognition of face and body in the wild with large 3D rotation, illumination change, partial occlusion, unknown/changing background, and aging; especially large 3D rotation robust face and gesture recognition;
7. Motion analysis, tracking and extraction of face and body models from image sequences captured by mobile devices;
8. Face, gait, and action recognition in low-quality (blurred for instance), or low-resolution video from fixed or mobile cameras;
9. Novel mathematical modeling and algorithms, sensors and modalities for face & body gesture/action representation, analysis, and recognition for cross-domain social media;
10. Social/Psychological studies that can assist in understanding computational modeling and building better automated face and gesture systems for interaction purposes;
11. Novel social applications based on the robust detection, tracking and recognition of face, body, and action;
12. Face and gesture analysis for sentiment analysis in social media;
13. Other applications of face and gesture analysis in social media content understanding.
*Important Dates:
Submission Deadline: July 20, 2017
Notification: August 15, 2017
Camera Ready: August 20, 2017
*Honorary General Chair:
Thomas S. Huang, University of Illinois at Urbana-Champaign, USA
*General Co-Chairs
Dimitris N. Metaxas, Rutgers, The State University of New Jersey, USA
Yun Raymond Fu, Northeastern University, Boston, USA
*Workshop Co-Chairs
Mohammad Soleymani, Swiss Center for Affective Sciences, Switzerland
Ming Shao, University of Massachusetts Dartmouth, USA
Zhangyang (Atlas) Wang, Texas A&M University, USA
*Web and Publicity Co-Chairs
Zhengming Ding, Northeastern University, Boston, USA
Sheng Li, Northeastern University, Boston, USA