7,341 research outputs found
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper, we propose an Enriched Long-term Recurrent
Convolutional Network (ELRCN) that first encodes each micro-expression frame
into a feature vector through CNN module(s), then predicts the micro-expression
by passing the feature vector through a Long Short-term Memory (LSTM) module.
The framework contains two different network variants: (1) Channel-wise
stacking of input data for spatial enrichment, (2) Feature-wise stacking of
features for temporal enrichment. We demonstrate that the proposed approach is
able to achieve reasonably good performance, without data augmentation. In
addition, we also present ablation studies conducted on the framework and
visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th
IEEE Facial & Gesture 201
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations
A dynamic texture based approach to recognition of facial actions and their temporal models
In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amounts of image data, while being robust to noise and other
undesired artifacts. Potentially, this makes VAEs a suitable approach for
learning facial features for AU intensity estimation. Yet, most existing
VAE-based methods apply classifiers learned separately from the encoded
features. By contrast, the non-parametric (probabilistic) approaches, such as
Gaussian Processes (GPs), typically outperform their parametric counterparts,
but cannot deal easily with large amounts of data. To this end, we propose a
novel VAE semi-parametric modeling framework, named DeepCoder, which combines
the modeling power of parametric (convolutional) and nonparametric (ordinal
GPs) VAEs, for joint learning of (1) latent representations at multiple levels
in a task hierarchy1, and (2) classification of multiple ordinal outputs. We
show on benchmark datasets for AU intensity estimation that the proposed
DeepCoder outperforms the state-of-the-art approaches, and related VAEs and
deep learning models.Comment: ICCV 2017 - accepte
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