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FacesMM 2019 : The 2nd IEEE International Workshop on Faces in Multimedia (2019 ICME Workshop)


When Jul 8, 2019 - Jul 12, 2019
Where Shanghai, China
Submission Deadline Mar 1, 2019
Notification Due Mar 20, 2019
Final Version Due Apr 15, 2019
Categories    machine vision   facial recognition   deep learning   modeling & analysis of faces

Call For Papers

IEEE international workshop on in conjunction with 2019 ICME

2nd Workshop on Faces in Multimedia (FacesMM)

-- To Automatically Synthesize, Recognize, Understand Faces in the Wild

# Call For Papers

There has been remarkable advances in facial recognition technologies the past several years due to the rapid development of deep learning and large-scale, labeled face collections. Thus, there are now evermore challenging image and video collections to solve emerging problems in the fields of faces and multimedia. In parallel to face recognition, researchers continue to show an increasing interest in topic of face synthesis. Works have been done using imagery, videos, and various other modalities (e.g., hand sketches, 3D models, view-points): some focus on the individual or individuals (e.g., with/without makeup, age varying, predicting a child appearance from parents, face swapping), while others leverage generative modeling for semi-supervised learning of recognition or detection systems. Besides, generative modeling are methodologies to automatically interrupt and analyze faces for a better understanding of visual context (e.g., relationships of persons in a photo, age estimation, occupation recognition). It is an age where many creative approaches and views are proposed for face synthesizing. Also, various advances are being made in other technologies involving automatic face understanding: face tracking (e.g., landmark detection, facial expression analysis, face detection), face characterization (e.g., behavioral understanding, emotion recognition), facial characteristic analysis (e.g., gait, age, gender and ethnicity recognition), group understanding via social cues (e.g., kinship), and visual sentiment analysis (e.g., temperament, arrangement). The ability to model with high certainty has significant value in both the scientific communities and the commercial market, with applications spanning topics of HCI, social-media analytics, video indexing, visual surveillance, and online vision.

The 2nd Workshop on Faces in Multimedia (FacesMM) serves a forum for researchers to review the recent progress the automatic face understanding and synthesizing in multimedia. Special interest will be given to generative-based modeling. The workshop will include two keynotes, along with peer-reviewed papers (oral and poster). Novel high-quality contributions are solicited on the following topics:

Face synthesis and morphing; works on generative modeling;

Soft biometrics; profiling faces: age, gender, ethnicity, personality, kinship, occupation, and beauty ranking;

Deep learning practice for social face problems with ambiguity including kinship verification, family recognition and retrieval;

Discovery of the social groups from faces and the context;

Mining social face relations through metadata as well as visual information;

Tracking and extraction and analysis of face models captured by mobile devices;

Face recognition in low-quality or low-resolution video or images;

Novel mathematical models & algorithms: sensors & modalities for face, body pose, action representation;

Analysis and recognition for cross-domain social media;

Novel social applications involving detection, tracking & recognition of faces;

Face analysis for sentiment analysis in social media;

Other applications involving face analysis in social media content.

# Previous FacesMM Workshops

Take a look back at last year's FacesMM workshop,

# Important Dates

1 March 2019 Submission Deadline
20 March 2019 Notification
15 April 2018 Camera-Ready Due

# Author Guidelines

Submissions handled via CMT website:

Following the guideline of ICME2019:

6 pages (including references)
Using ICME template

# Organizers

Yun Fu, Northeastern University,

Joseph Robinson, Northeastern University,

Ming Shao, University of Massachusetts (Dartmouth),

Siyu Xia, Southeast University (China), Nanjing,

# Contact

Joseph Robinson (
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

Ming Shao (
Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA

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