AIMC Topic: Face

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The accuracy of automated facial landmarking - a comparative study between Cliniface software and patch-based Convoluted Neural Network algorithm.

European journal of orthodontics
BACKGROUND: Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access soft...

Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.

Developmental science
Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to familiar versus novel faces in the first 6 months. However, by 12 months of age brain responses may not d...

3D DenseNet with temporal transition layer for heart rate estimation from real-life RGB videos.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and ...

Patch-based convolutional neural networks for automatic landmark detection of 3D facial images in clinical settings.

European journal of orthodontics
BACKGROUND: The facial landmark annotation of 3D facial images is crucial in clinical orthodontics and orthognathic surgeries for accurate diagnosis and treatment planning. While manual landmarking has traditionally been the gold standard, it is labo...

[Artificial intelligence model for diagnosis of coronary artery disease based on facial photos].

Zhonghua xin xue guan bing za zhi
To develop and validate an artificial intelligence (AI) diagnostic model for coronary artery disease based on facial photos. This study was a cross-sectional study. Patients who were scheduled to undergo coronary angiography (CAG) at Beijing Anzhen...

Deep convolutional neural networks are sensitive to face configuration.

Journal of vision
Deep convolutional neural networks (DCNNs) are remarkably accurate models of human face recognition. However, less is known about whether these models generate face representations similar to those used by humans. Sensitivity to facial configuration ...

An artificial intelligence powered study of enlarged facial pore prevalence on one million Chinese from different age groups and its correlation with environmental factors.

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: Enlarged pores are amidst one of the top cosmetic concerns, especially among Chinese. Many small-group studies have been conducted in understanding their prevalence and beauty relevance. Nonetheless, population-level investigations are st...

Improved optimizer with deep learning model for emotion detection and classification.

Mathematical biosciences and engineering : MBE
Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Non...

[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].

Zhonghua yi xue za zhi
To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. A cross-sectional study. Twenty-five narcolepsy type 1 patients who wer...