3,275 research outputs found
Learning Grimaces by Watching TV
Differently from computer vision systems which require explicit supervision,
humans can learn facial expressions by observing people in their environment.
In this paper, we look at how similar capabilities could be developed in
machine vision. As a starting point, we consider the problem of relating facial
expressions to objectively measurable events occurring in videos. In
particular, we consider a gameshow in which contestants play to win significant
sums of money. We extract events affecting the game and corresponding facial
expressions objectively and automatically from the videos, obtaining large
quantities of labelled data for our study. We also develop, using benchmarks
such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial
expression recognition, showing that pre-training on face verification data can
be highly beneficial for this task. Then, we extend these models to use facial
expressions to predict events in videos and learn nameable expressions from
them. The dataset and emotion recognition models are available at
http://www.robots.ox.ac.uk/~vgg/data/facevalueComment: British Machine Vision Conference (BMVC) 201
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
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
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