3,686 research outputs found
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
Frame-level visual features are generally aggregated in time with the
techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust
video-level representation. We here introduce a learnable aggregation technique
whose primary objective is to retain short-time temporal structure between
frame-level features and their spatial interdependencies in the representation.
Also, it can be easily adapted to the cases where there have very scarce
training samples. We evaluate the method on a real-fake expression prediction
dataset to demonstrate its superiority. Our method obtains 65% score on the
test dataset in the official MAP evaluation and there is only one misclassified
decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) .
Lastly, we believe that this method can be extended to different problems such
as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop
LEARNet Dynamic Imaging Network for Micro Expression Recognition
Unlike prevalent facial expressions, micro expressions have subtle,
involuntary muscle movements which are short-lived in nature. These minute
muscle movements reflect true emotions of a person. Due to the short duration
and low intensity, these micro-expressions are very difficult to perceive and
interpret correctly. In this paper, we propose the dynamic representation of
micro-expressions to preserve facial movement information of a video in a
single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to
capture micro-level features of an expression in the facial region. The LEARNet
refines the salient expression features in accretive manner by incorporating
accretion layers (AL) in the network. The response of the AL holds the hybrid
feature maps generated by prior laterally connected convolution layers.
Moreover, LEARNet architecture incorporates the cross decoupled relationship
between convolution layers which helps in preserving the tiny but influential
facial muscle change information. The visual responses of the proposed LEARNet
depict the effectiveness of the system by preserving both high- and micro-level
edge features of facial expression. The effectiveness of the proposed LEARNet
is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC.
The experimental results after investigation show a significant improvement of
4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II,
CAS(ME)^2 and SMIC datasets respectively.Comment: Dynamic imaging, accretion, lateral, micro expression recognitio
Less is More: Micro-expression Recognition from Video using Apex Frame
Despite recent interest and advances in facial micro-expression research,
there is still plenty room for improvement in terms of micro-expression
recognition. Conventional feature extraction approaches for micro-expression
video consider either the whole video sequence or a part of it, for
representation. However, with the high-speed video capture of micro-expressions
(100-200 fps), are all frames necessary to provide a sufficiently meaningful
representation? Is the luxury of data a bane to accurate recognition? A novel
proposition is presented in this paper, whereby we utilize only two images per
video: the apex frame and the onset frame. The apex frame of a video contains
the highest intensity of expression changes among all frames, while the onset
is the perfect choice of a reference frame with neutral expression. A new
feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to
encode essential expressiveness of the apex frame. We evaluated the proposed
method on five micro-expression databases: CAS(ME), CASME II, SMIC-HS,
SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with
our proposed technique achieving a state-of-the-art F1-score recognition
performance of 61% and 62% in the high frame rate CASME II and SMIC-HS
databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment
of grant support adde
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