AIMC Topic: Face

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Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

International journal of computer assisted radiology and surgery
PURPOSE: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and how the face passively responds to the bony movement. Currently, finite element method (FEM) is the standard for predicting facial deformation. Deep l...

Automated Facial Expression Recognition Framework Using Deep Learning.

Journal of healthcare engineering
Facial expression is one of the most significant elements which can tell us about the mental state of any person. A human can convey approximately 55% of information nonverbally and the remaining almost 45% through verbal communication. Automatic fac...

Motion Fatigue State Detection Based on Neural Networks.

Computational intelligence and neuroscience
Aiming at the problem of fatigue state detection at the back of sports, a cascade deep learning detection system structure is designed, and a convolutional neural network fatigue state detection model based on multiscale pooling is proposed. Firstly,...

AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN.

Sensors (Basel, Switzerland)
To address the problem of automatically detecting and removing the mask without user interaction, we present a GAN-based automatic approach for face de-occlusion, called Automatic Mask Generation Network for Face De-occlusion Using Stacked Generative...

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nature genetics
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient pho...

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

Scientific reports
Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In genera...

Face Detection Algorithm Based on Double-Channel CNN with Occlusion Perceptron.

Computational intelligence and neuroscience
Aiming at the problem of low accuracy of face detection under complex occlusion conditions, a double-channel occlusion perceptron neural network model was proposed. The area occlusion judgment unit is designed and integrated into the VGG16 network to...

Improving Face-Based Age Estimation With Attention-Based Dynamic Patch Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignor...

Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphe...

Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space.

Scientific reports
Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that uses well-controlled yet highly naturalistic stimuli with a priori known ...