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Facial Recognition

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MCCA-VNet: A Vit-Based Deep Learning Approach for Micro-Expression Recognition Based on Facial Coding.

Sensors (Basel, Switzerland)
In terms of facial expressions, micro-expressions are more realistic than macro-expressions and provide more valuable information, which can be widely used in psychological counseling and clinical diagnosis. In the past few years, deep learning metho...

Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.

Journal of psychiatric research
OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients with drug-naive schizophrenia (DN-SCZ) or chronic schizophrenia (C-SCZ) from healthy controls (HCs), a...

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization.

Sensors (Basel, Switzerland)
With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep lear...

Improved facial emotion recognition model based on a novel deep convolutional structure.

Scientific reports
Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in qualit...

Decoding face identity: A reverse-correlation approach using deep learning.

Cognition
Face recognition is crucial for social interactions. Traditional approaches primarily rely on subjective judgment, utilizing a pre-selected set of facial features based on literature or intuition to identify critical facial features for face recognit...

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 ...

View-symmetric representations of faces in human and artificial neural networks.

Neuropsychologia
View symmetry has been suggested to be an important intermediate representation between view-specific and view-invariant representations of faces in the human brain. Here, we compared view-symmetry in humans and a deep convolutional neural network (D...

A facial expression recognition network using hybrid feature extraction.

PloS one
Facial expression recognition faces great challenges due to factors such as face similarity, image quality, and age variation. Although various existing end-to-end Convolutional Neural Network (CNN) architectures have achieved good classification res...

Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children.

Brain research bulletin
BACKGROUND: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children.

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...