AIMC Topic: Face

Clear Filters Showing 271 to 280 of 425 articles

Information-Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning.

Computational intelligence and neuroscience
This paper describes a new image generation algorithm based on generative adversarial network. With an information-theoretic extension to the autoencoder-based discriminator, this new algorithm is able to learn interpretable representations from the ...

CNN-Based Multimodal Human Recognition in Surveillance Environments.

Sensors (Basel, Switzerland)
In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research bein...

Discriminant Functional Learning of Color Features for the Recognition of Facial Action Units and Their Intensities.

IEEE transactions on pattern analysis and machine intelligence
Color is a fundamental image feature of facial expressions. For example, when we furrow our eyebrows in anger, blood rushes in, turning some face areas red; or when one goes white in fear as a result of the drainage of blood from the face. Surprising...

Representation Learning by Rotating Your Faces.

IEEE transactions on pattern analysis and machine intelligence
The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant represe...

A hierarchical multimodal system for motion analysis in patients with epilepsy.

Epilepsy & behavior : E&B
During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying cerebral networks involved. When patients with drug-resistant epilepsy are monitored to assess thei...

Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition.

Computational intelligence and neuroscience
Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this...

Deep Neural Representation Guided Face Sketch Synthesis.

IEEE transactions on visualization and computer graphics
Face sketch synthesis shows great applications in a lot of fields such as online entertainment and suspects identification. Existing face sketch synthesis methods learn the patch-wise sketch style from the training dataset containing photo-sketch pai...

Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation.

Scientific reports
Patients' postoperative facial swelling following third molars extraction may have both biological impacts and social impacts. The purpose of this study was to evaluate the accuracy of artificial neural networks in the prediction of the postoperative...

Fine-Grained Face Annotation Using Deep Multi-Task CNN.

Sensors (Basel, Switzerland)
We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct...

Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age.

International journal of oral and maxillofacial surgery
This observational study aimed to use artificial intelligence to describe the impact of orthognathic treatment on facial attractiveness and age appearance. Pre- and post-treatment photographs (n=2164) of 146 consecutive orthognathic patients were col...