AIMC Topic: Automated Facial Recognition

Clear Filters Showing 31 to 40 of 70 articles

Face anti-spoofing with cross-stage relation enhancement and spoof material perception.

Neural networks : the official journal of the International Neural Network Society
Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access control, electronic payment, and security surveillance systems. Face anti-spoofing requires the integra...

Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

Sleep & breathing = Schlaf & Atmung
PURPOSE: The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combi...

A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores.

Forensic science international
Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier bas...

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems.

Computational and mathematical methods in medicine
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authe...

Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction.

Computational and mathematical methods in medicine
The application of face detection and recognition technology in security monitoring systems has made a huge contribution to public security. Face detection is an essential first step in many face analysis systems. In complex scenes, the accuracy of f...

Class-Variant Margin Normalized Softmax Loss for Deep Face Recognition.

IEEE transactions on neural networks and learning systems
In deep face recognition, the commonly used softmax loss and its newly proposed variations are not yet sufficiently effective to handle the class imbalance and softmax saturation issues during the training process while extracting discriminative feat...

AAN-Face: Attention Augmented Networks for Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Convolutional neural networks are capable of extracting powerful representations for face recognition. However, they tend to suffer from poor generalization due to imbalanced data distributions where a small number of classes are over-represented (e....

Structure-Coherent Deep Feature Learning for Robust Face Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make th...

Adaptive Weighting of Handcrafted Feature Losses for Facial Expression Recognition.

IEEE transactions on cybernetics
Due to the importance of facial expressions in human-machine interaction, a number of handcrafted features and deep neural networks have been developed for facial expression recognition. While a few studies have shown the similarity between the handc...