16,283 research outputs found
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Relative Facial Action Unit Detection
This paper presents a subject-independent facial action unit (AU) detection
method by introducing the concept of relative AU detection, for scenarios where
the neutral face is not provided. We propose a new classification objective
function which analyzes the temporal neighborhood of the current frame to
decide if the expression recently increased, decreased or showed no change.
This approach is a significant change from the conventional absolute method
which decides about AU classification using the current frame, without an
explicit comparison with its neighboring frames. Our proposed method improves
robustness to individual differences such as face scale and shape, age-related
wrinkles, and transitions among expressions (e.g., lower intensity of
expressions). Our experiments on three publicly available datasets (Extended
Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement
of our approach over conventional absolute techniques. Keywords: facial action
coding system (FACS); relative facial action unit detection; temporal
information;Comment: Accepted at IEEE Winter Conference on Applications of Computer
Vision, Steamboat Springs Colorado, USA, 201
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