AIMC Topic: Face

Clear Filters Showing 251 to 260 of 425 articles

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model.

Neural computation
A recent paper (Chang & Tsao, 2017) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition ...

Hands-Free User Interface for AR/VR Devices Exploiting Wearer's Facial Gestures Using Unsupervised Deep Learning.

Sensors (Basel, Switzerland)
Developing a user interface (UI) suitable for headset environments is one of the challenges in the field of augmented reality (AR) technologies. This study proposes a hands-free UI for an AR headset that exploits facial gestures of the wearer to reco...

Face Hallucination Using Cascaded Super-Resolution and Identity Priors.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucinatio...

Automated grading of acne vulgaris by deep learning with convolutional neural networks.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability.

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

Neuroendocrinology
BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up.

Depression recognition using machine learning methods with different feature generation strategies.

Artificial intelligence in medicine
The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method...

Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis.

IEEE transactions on cybernetics
Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each ta...

Robust RGB-D Face Recognition Using Attribute-Aware Loss.

IEEE transactions on pattern analysis and machine intelligence
Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within th...

Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes.

IEEE transactions on pattern analysis and machine intelligence
Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an exemplary dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patc...

Artificial Intelligence based facial recognition for Mood Charting among men on life style modification and it's correlation with cortisol.

Asian journal of psychiatry
UNLABELLED: Today, clinicians and researchers believe that mood disorders in children and adolescents remain one of the most under diagnosed mental health problems. Mood disorders in adolescents also put them at risk for other conditions that may per...