Affective computing (AC) is a unique discipline which attempts to model affect using one or 
multiple modalities by drawing on techniques from many different fields. AC often deals with 
problems that are known to be very complex and multi-dimensional, involving different kinds of 
data (numeric, symbolic, visual etc.). However, with the advancement of machine learning techniques,
a lot of those problems are now becoming more tractable.
 
The purpose of this workshop is to engage the machine learning and affective computing communities 
towards solving the problems related to understanding and modeling social affective behaviors. We 
welcome the participation of researchers from diverse fields, including signal processing and 
pattern recognition, statistical machine learning, human-computer interaction, human-robot 
interaction, robotics, conversational agents, experimental psychology, and decision making.

There is a need for a set of high standards for recognizing and understanding affect. At the same 
time, these standards need to take into account that the expectations and validations in this area 
may be different than in traditional research on machine learning. This should be reflected in the 
design of machine learning techniques used to tackle these problems. For example, affective data 
sets are known to be noisy, high dimensional, and incomplete. Classes may overlap. Affective 
behaviors are often person specific and require temporal modeling with real-time performance. 
This first edition of the ACII Workshop on Machine Learning for Affective

Computing will be the perfect venue to invoke such discussions and engage the community towards 
design and validation of learning techniques for affective sensing.

 

Topics:

--------------

 
We invite submissions of high-quality papers describing novel research on machine learning applied
 to human affective computing and related topics. Suitable themes include, but are not limited to:

* Automatic recognition of spontaneous affective states

* Multimodal integration of speech, facial expressions and body posture

* Audio-visual signal processing

* Context-based understanding of affective state

* Feature selection and engineering

* Online user adaptation and calibration

* Unsupervised analysis of affective behaviors

* Spontaneous datasets for affective learning

* Evaluation metrics for probabilistic modeling of affective states

 
The paper should not include work previously published elsewhere or under review in other 
conferences/journals.

Program Committee (partial list):

--------------------------------------------

Alessandro Vinciarelli (Glasgow)

Ashish Kapoor (Microsoft Research)

Athanassios (Nassos) Katsamanis (USC)

Carlos Busso (UT-Dallas)

Dan Bohus (Microsoft Research)

Fernando De la Torre (CMU)

Ginevra Castellano (Queen Mary University)

Jean-Claude Martin, (LIMSI-CNRS)

France Jeffrey Cohn (Pitt)

Jonathan Gratch (USC)

Julia Hirschberg (Columbia)

Magalie Ochs (Telecom Paris Tech)

Matthew Turk (UCSB)

Nicu Sebe (Trento)

Peter Robinson (Cambridge)

Rana el-Kaliouby (MIT)

Ross Beveridge (Colorado State)

Shri Narayanan (USC)

Tanzeem Chowdhury (Dartmouth)

Winslow Burleson (ASU)

 

 

Submission guidelines:

--------------------------------------------

 

Papers will be published in the proceedings of ACII 2011 by Springer. Papers should not exceed 10 
pages and should be formatted to the Springer LNCS formatting guidelines 
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0/ Papers must be submitted as PDF 
through the EasyChair Conference system, which can be accessed via the web at

https://www.easychair.org/account/signin.cgi?conf=acii20110.


Important dates:

-------------------------------

Submission deadline: June 24th, 2011, 11:59pm PDT

Notification of acceptance: July 15th, 2011

Camera ready version due: July 25th, 2011

Workshop: October 9th, 2011

 

Organizers:

----------------------

M. Ehsan Hoque (MIT)

Dan McDuff (MIT)

Louis Philippe Morency (USC)

Rosalind Picard (MIT)