AIMC Topic: Face

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Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function.

PloS one
With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someo...

Which Visual Modality Is Important When Judging the Naturalness of the Agent (Artificial Versus Human Intelligence) Providing Recommendations in the Symbolic Consumption Context?

Sensors (Basel, Switzerland)
This study aimed to explore how the type and visual modality of a recommendation agent's identity affect male university students' (1) self-reported responses to agent-recommended symbolic brand in evaluating the naturalness of virtual agents, human,...

End-to-End Training for Compound Expression Recognition.

Sensors (Basel, Switzerland)
For a long time, expressions have been something that human beings are proud of. That is an essential difference between us and machines. With the development of computers, we are more eager to develop communication between humans and machines, espec...

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications.

Sensors (Basel, Switzerland)
Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be ...

Sparse Spatiotemporal Descriptor for Micro-Expression Recognition Using Enhanced Local Cube Binary Pattern.

Sensors (Basel, Switzerland)
As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, an...

Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning.

Frontiers in endocrinology
Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate ...

Age-Related Differences in Fixation Pattern on a Companion Robot.

Sensors (Basel, Switzerland)
Recent studies have addressed the various benefits of companion robots and expanded the research scope to their design. However, the viewpoints of older adults have not been deeply investigated. Therefore, this study aimed to examine the distinctive ...

Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning.

Journal of hematology & oncology
Due to acromegaly's insidious onset and slow progression, its diagnosis is usually delayed, thus causing severe complications and treatment difficulty. A convenient screening method is imperative. Based on our previous work, we herein developed a new...

Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition meth...

Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.

American journal of medical genetics. Part A
Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal uniparental disomy (UPD), UBE3A pathogenic variants, or imprinting defects. Current methods of differe...