2 research outputs found
Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition
Micro expression recognition (MER)is a very challenging task as the
expression lives very short in nature and demands feature modeling with the
involvement of both spatial and temporal dynamics. Existing MER systems exploit
CNN networks to spot the significant features of minor muscle movements and
subtle changes. However, existing networks fail to establish a relationship
between spatial features of facial appearance and temporal variations of facial
dynamics. Thus, these networks were not able to effectively capture minute
variations and subtle changes in expressive regions. To address these issues,
we introduce an active imaging concept to segregate active changes in
expressive regions of a video into a single frame while preserving facial
appearance information. Moreover, we propose a shallow CNN network: hybrid
local receptive field based augmented learning network (OrigiNet) that
efficiently learns significant features of the micro-expressions in a video. In
this paper, we propose a new refined rectified linear unit (RReLU), which
overcome the problem of vanishing gradient and dying ReLU. RReLU extends the
range of derivatives as compared to existing activation functions. The RReLU
not only injects a nonlinearity but also captures the true edges by imposing
additive and multiplicative property. Furthermore, we present an augmented
feature learning block to improve the learning capabilities of the network by
embedding two parallel fully connected layers. The performance of proposed
OrigiNet is evaluated by conducting leave one subject out experiments on four
comprehensive ME datasets. The experimental results demonstrate that OrigiNet
outperformed state-of-the-art techniques with less computational complexity
Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System
Owing to the development and advancement of artificial intelligence, numerous
works were established in the human facial expression recognition system.
Meanwhile, the detection and classification of micro-expressions are attracting
attentions from various research communities in the recent few years. In this
paper, we first review the processes of a conventional optical-flow-based
recognition system, which comprised of facial landmarks annotations, optical
flow guided images computation, features extraction and emotion class
categorization. Secondly, a few approaches have been proposed to improve the
feature extraction part, such as exploiting GAN to generate more image samples.
Particularly, several variations of optical flow are computed in order to
generate optimal images to lead to high recognition accuracy. Next, GAN, a
combination of Generator and Discriminator, is utilized to generate new "fake"
images to increase the sample size. Thirdly, a modified state-of-the-art
Convolutional neural networks is proposed. To verify the effectiveness of the
the proposed method, the results are evaluated on spontaneous micro-expression
databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy
performance metrics are reported in this paper.Comment: 15 pages, 16 figures, 6 table