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Found 13 papers, 1 papers with code
eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View Synthesis
Considering the motion induced by head variation as noise and the motion induced by facial expression as the relevant information, our model is trained to filter out the noisy motion in order to retain only the motion related to facial expression.
Facial Expression Recognition
Facial Expression Recognition (FER)
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition
In this work, we use CSNNs trained in an unsupervised manner with the Spike Timing-Dependent Plasticity (STDP) rule, and we introduce, for the first time, Spiking Separated Spatial and Temporal Convolutions (S3TCs) for the sake of reducing the number of parameters required for video analysis.
Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks
Then, we introduce a training architecture called Paired Competing Neurons (PCN) to further enhance the learning capabilities of our classification layer trained with S2-STDP.
Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
Implementing this model with unsupervised STDP-based CSNNs allows us to further study the performance of these networks with video analysis.
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed physical perturbations.
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition
We compare the performance of this model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann datasets.
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction
To handle occlusions, solutions based on the reconstruction of the occluded part of the face have been proposed.
Dynamic Facial Expression Recognition
Facial Expression Recognition
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Improving STDP-based Visual Feature Learning with Whitening
Experiments on CIFAR-10 show that whitening allows STDP to learn visual features that are closer to the ones learned with standard neural networks, with a significantly increased classification performance as compared to DoG filtering.
Impact of facial landmark localization on facial expression recognition
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks?
Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation
The main innovation brought by this contribution consists in exploiting the specificities of facial movement propagation for recognizing expressions in presence of important occlusions.
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition.
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware.
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures.
