AIMC Topic: Automated Facial Recognition

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Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person.

IEEE transactions on neural networks and learning systems
This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisan...

CapsField: Light Field-Based Face and Expression Recognition in the Wild Using Capsule Routing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometric...

Deep Coattention-Based Comparator for Relative Representation Learning in Person Re-Identification.

IEEE transactions on neural networks and learning systems
Person re-identification (re-ID) favors discriminative representations over unseen shots to recognize identities in disjoint camera views. Effective methods are developed via pair-wise similarity learning to detect a fixed set of region features, whi...

Face Hallucination With Finishing Touches.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non...

Generating photo-realistic training data to improve face recognition accuracy.

Neural networks : the official journal of the International Neural Network Society
Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be tra...

Robust facial landmark detection by cross-order cross-semantic deep network.

Neural networks : the official journal of the International Neural Network Society
Recently, convolutional neural networks (CNNs)-based facial landmark detection methods have achieved great success. However, most of existing CNN-based facial landmark detection methods have not attempted to activate multiple correlated facial parts ...

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization.

Sensors (Basel, Switzerland)
Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet wa...

A Novel Use of Artificial Intelligence to Examine Diversity and Hospital Performance.

The Journal of surgical research
BACKGROUND: The US population is becoming more racially and ethnically diverse. Research suggests that cultural diversity within organizations can increase team potency and performance, yet this theory has not been explored in the field of surgery. F...

Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition.

Sensors (Basel, Switzerland)
Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER's critical problem with traditional local...

FPGAN: Face de-identification method with generative adversarial networks for social robots.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its converge...