A review of face recognition methods using deep learning network

Ak, Dutta; Jain, Vanita

doi:10.1080/02522667.2019.1582875

Cited by 24 publications

(5 citation statements)

References 15 publications

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“…Face detection is to extract the salient features of the face image, and face recognition is to extract the features of the face; the existing features are compared to determine which kind of face the feature belongs to, and the two have essential differences. [16,17].…”

Section: Related Workmentioning

confidence: 99%

Analysis of University Education Management Based on Artificial Intelligence

Zhu

2022

Security and Communication Networks

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With the rapid development of information technology, the process of informatization of education management has been accelerated. In this context, more and more education management information systems have been used in education management, providing a lot of data support for education decision-making. In addition, the development of artificial intelligence has greatly changed the way people work and live. Intelligence has emerged in various fields, bringing great convenience to people, especially the university education management. This study will integrate artificial intelligence and university classroom teaching and apply it in the field of education management. In particular, the proposed intelligent education management system mainly includes three submodules: preclass attendance, in-class state monitoring, and after-class online learning. The main function of the preclass attendance module is that half an hour before the class starts, the camera captures students’ video information and sends it back to the convolutional neural network (CNN) model for face recognition processing. In class, the state detection module is mainly based on face recognition to judge the state of students. The after-class module analyzes the evaluation information of students’ online learning to provide a teaching reference for the school. The system proposed in this study can improve the quality of students’ classroom learning and teachers’ monitoring of the quality of students’ classroom.

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Section: Related Workmentioning

confidence: 99%

Analysis of University Education Management Based on Artificial Intelligence

Zhu

2022

Security and Communication Networks

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“…They done a comprehensive review about the challenges, applications of face detection system, feature based and image based FD approaches, Statistical based FD system and the databases used for FD and concluded with the future directions for research on FD. Arunkumar&jain [48] reviewed and compared the FD techniques that are applicable for face detection from images and videos. They also listed the Face recognition models that are frequently used and the databases that are publically available for face recognition.…”

Section: Review About the Face Detection Approaches Using Ml/ Deep Learning Algorithmsmentioning

confidence: 99%

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Anandh¹

2021

TURCOMAT

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In worldwide, Visually Impaired Persons (VIP) are facing several issue related to visual impairment and blindness and they are assisted with technical inventions. Based on the survey from World Health Organization (WHO), around 2.2 billion peoples are suffered from visual impairment and among them 1 million peoples are suffered by blindness. Vision is the major sensing organ of the human and to assist the VIP regarding this, there are various digital vision products are in the market which is based on digital technologies and advanced algorithms. This will transform the VIP’s vision world into audio to get to know about their surroundings includes objects, motion, obstacles and spatial locations. The objective of this paper is to provide the detailed survey about the existing object recognition, face recognition and text to voice recognition methods proposed to assist VIP. Due to the increase development of machine learning and deep learning algorithms, the digital image recognition and object recognition are become more efficient. These advanced technologies can make sure to assist the VIP to detect and recognize the people face and objects in front of them in the form of audio that are practiced by them on daily basis.

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“…Deep learning has become the predominant solution for most facial analysis problems. The preference for deep learning models in the computer vision field is due to the robustness of architectures such as convolutional neural networks or residual connections, which effectively extracts all the necessary facial features without manually engineering the features using several filters [1,2]. Gender estimation is the idea of training a machine learning model on thousands of labelled facial images to produce a function that maps an input image X to a corresponding label Y.…”

Section: Introductionmentioning

confidence: 99%

Artificially Generated Facial Images for Gender Classification Using Deep Learning

Raman¹,

ELKarazle²,

Then³

2023

Computer Systems Science and Engineering

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Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common. A typical gender classifier requires many training samples to learn as many distinguishable features as possible. However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law. In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset of facial images is proposed. We acquire a pre-trained Style-Generative Adversarial Networks (StyleGAN) generator and use it to create a dataset of facial images. We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart. We use this manuallylabelled dataset to train three facial gender classifiers, a custom-designed network, and two pre-trained networks based on the Visual Geometry Group designs (VGG16) and (VGG19). We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects. For testing, we use the UTKFace and the Kaggle gender dataset. Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods, which uses actual images of individuals. The average classification accuracy of each classifier is between 94% and 95%, which is similar to existing proposed methods.

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A review of face recognition methods using deep learning network

Cited by 24 publications

References 15 publications

Analysis of University Education Management Based on Artificial Intelligence

Analysis of University Education Management Based on Artificial Intelligence

Study Of Deep Learning Techniques For Differently Abled Applications And Detection Techniques

Artificially Generated Facial Images for Gender Classification Using Deep Learning

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