A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model

Su, Dongchu; Li, Yong; Zhao, Yunping; Xu, Rui; Yuan, Bo; Wu, Wenjuan

doi:10.1088/1742-6596/1693/1/012151

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VGG [23], the full name of visual geometry group, is a series of convolutional neural network models starting with VGG published by the department of science and engineering of Oxford university, which can be applied to face recognition [24,25,26], image classification [27,28] and other aspects. The original purpose of studying the depth of convolution network is to find out how the depth of convolution network affects the accuracy and accuracy of large-scale image classification and recognition.…”

Section: B Vgg Network Overviewmentioning

confidence: 99%

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Wang,

Zhang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Increased physical activity can help reduce the occurrence of cardiovascular disease. However, cardiovascular disease during strenuous exercise also brings certain risks, so a convenient and effective method is needed to accurately identify heart rate. Due to the low amplitude characteristics of ECG signals, automatic classification of the imperceptibility and irregularities of ECG signals remains a great challenge. To address this issue, we propose an automatic heart rate detection method using a two-dimensional convolutional neural network. First, the ECG data is preprocessed to convert the multi-lead single-channel ECG data into dual-channel ECG data. Then, the ECG image is input into the convolutional neural network. To enhance the diagnostic accuracy of an abnormal heart rate diagnosis model, this study integrates a multiscale pyramid module into the network to fully extract image contextual information. Experimental analysis utilizes MIT-BIH and sudden cardiac death holter public datasets for training and testing. The results show that the final classification accuracy reaches 99.12% and 98.40%, respectively. The method requires no manual pre-processing of converted ECG images, has high accuracy, can effectively monitor abnormal heart rate, and minimize sudden death caused by abnormal heart rate.

show abstract

Section: B Vgg Network Overviewmentioning

confidence: 99%

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Wang,

Zhang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In a recent study, Su et al [31] proposed a dual-channel image-based method. The original VGG-like model, the ResNet called by Dlib, MobileFaceNet, and the new VGG-cut model were compared on a RISC-V SoC to emulate an embedded environment.…”

Section: Related Workmentioning

confidence: 99%

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Kanawade,

Surve,

Khonde

et al. 2023

ISI

View full text Add to dashboard Cite

In the realm of surveillance, closed-circuit television (CCTV) cameras serve as a vigilant watch over unfamiliar entities. However, the unpredictability of such entities necessitates continuous human monitoring, an endeavor prone to error and demanding of significant resources. The automation of this process through face recognition could alleviate these burdens, provided the system delivers high precision and rapid judgment capabilities. This study presents a novel solution to these challenges: an automated human recognition and verification surveillance system, founded on a max-voting ensemble method. This innovative approach amalgamates five influential feature extraction models: VGGFace, FaceNet, FaceNet-512, Dlib, and Arcface, with a support vector machine deployed for classification. The proposed system was subjected to rigorous testing on the AT&T, faces94, Grimace, Georgia Tech, and FaceScrub datasets, demonstrating an impressive accuracy of 100% on the AT&T, faces94, and Grimace datasets, and 99.3% and 98% on the Georgia Tech and FaceScrub datasets, respectively. The system's performance was further enhanced through a re-verification technique, which facilitated swift and precise prediction of unknown entities in real time. This study thus contributes a significant advancement to the field of automated surveillance, offering a potent tool for efficient, accurate human recognition.

show abstract

Vision-based multi-model facial emotion recognition psychotherapy assistant system

2023

AIP Conference Proceedings

View full text Add to dashboard Cite

A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model

Cited by 4 publications

References 3 publications

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Vision-based multi-model facial emotion recognition psychotherapy assistant system

Contact Info

Product

Resources

About