14 research outputs found
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
SSSDET: Simple Short and Shallow Network for Resource Efficient Vehicle Detection in Aerial Scenes
Detection of small-sized targets is of paramount importance in many aerial
vision-based applications. The commonly deployed low cost unmanned aerial
vehicles (UAVs) for aerial scene analysis are highly resource constrained in
nature. In this paper we propose a simple short and shallow network (SSSDet) to
robustly detect and classify small-sized vehicles in aerial scenes. The
proposed SSSDet is up to 4x faster, requires 4.4x less FLOPs, has 30x less
parameters, requires 31x less memory space and provides better accuracy in
comparison to existing state-of-the-art detectors. Thus, it is more suitable
for hardware implementation in real-time applications. We also created a new
airborne image dataset (ABD) by annotating 1396 new objects in 79 aerial images
for our experiments. The effectiveness of the proposed method is validated on
the existing VEDAI, DLR-3K, DOTA and Combined dataset. The SSSDet outperforms
state-of-the-art detectors in term of accuracy, speed, compute and memory
efficiency.Comment: International Conference on Image Processing (ICIP) 2019, Taipei,
Taiwa
Efficient Neural Architecture Search for Emotion Recognition
Automated human emotion recognition from facial expressions is a well-studied
problem and still remains a very challenging task. Some efficient or accurate
deep learning models have been presented in the literature. However, it is
quite difficult to design a model that is both efficient and accurate at the
same time. Moreover, identifying the minute feature variations in facial
regions for both macro and micro-expressions requires expertise in network
design. In this paper, we proposed to search for a highly efficient and robust
neural architecture for both macro and micro-level facial expression
recognition. To the best of our knowledge, this is the first attempt to design
a NAS-based solution for both macro and micro-expression recognition. We
produce lightweight models with a gradient-based architecture search algorithm.
To maintain consistency between macro and micro-expressions, we utilize dynamic
imaging and convert microexpression sequences into a single frame, preserving
the spatiotemporal features in the facial regions. The EmoNAS has evaluated
over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS
CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II,
CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the
existing state-of-the-art methods and perform very well in terms of speed and
space complexity