2,345 research outputs found
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
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
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
To facilitate the analysis of human actions, interactions and emotions, we
compute a 3D model of human body pose, hand pose, and facial expression from a
single monocular image. To achieve this, we use thousands of 3D scans to train
a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with
fully articulated hands and an expressive face. Learning to regress the
parameters of SMPL-X directly from images is challenging without paired images
and 3D ground truth. Consequently, we follow the approach of SMPLify, which
estimates 2D features and then optimizes model parameters to fit the features.
We improve on SMPLify in several significant ways: (1) we detect 2D features
corresponding to the face, hands, and feet and fit the full SMPL-X model to
these; (2) we train a new neural network pose prior using a large MoCap
dataset; (3) we define a new interpenetration penalty that is both fast and
accurate; (4) we automatically detect gender and the appropriate body models
(male, female, or neutral); (5) our PyTorch implementation achieves a speedup
of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to
both controlled images and images in the wild. We evaluate 3D accuracy on a new
curated dataset comprising 100 images with pseudo ground-truth. This is a step
towards automatic expressive human capture from monocular RGB data. The models,
code, and data are available for research purposes at
https://smpl-x.is.tue.mpg.de.Comment: To appear in CVPR 201
LOMo: Latent Ordinal Model for Facial Analysis in Videos
We study the problem of facial analysis in videos. We propose a novel weakly
supervised learning method that models the video event (expression, pain etc.)
as a sequence of automatically mined, discriminative sub-events (eg. onset and
offset phase for smile, brow lower and cheek raise for pain). 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 or temporal 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. In combination with complimentary features, we report
state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR
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