Face Recognition Based on Lightweight Convolutional Neural Networks

Liu, Wenting; Zhou, Li; Chen, Jie

doi:10.3390/info12050191

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…On the other hand, the MobileFaceNet was employed as benchmark for constructing lightweight CNN [28]. Diaz et al [8] examined the impact of developing lightweight face architectures of different real-world applications to https:// journal.uob.edu.bh serve as guidance for researchers in the community of face detection and recognition.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

Ahmed,

S. Alghareb

2024

IJCDS

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In recent years, deep learning-based algorithms have been immensely employed and tested in a variety of real-world applications. The efficacy of such algorithms has been thoroughly examined in a practical setting. In this paper, CNN-based deep learning approaches are utilized to recognize faces in real-time to identify faces with and without mask. We employ pre-trained algorithms (YOLOv2 and SSD) to identify people wearing a face mask, which enables a machine to perform recognition tasks while evolving through a learning method. Meanwhile, if there is more than one person in the scene, the one with the max score will be selected for classification. Thus, a hybrid approach that combines YOLOv2 and SSD algorithms to work in parallel is developed for masked-face extraction. Likewise, the Viola-Jones algorithm is used here to detect faces without mask and randomly select a single region of interest (ROI) to be stored for classification. All pre-processing algorithms work separately in parallel as reconstruction steps for preprocessing to crop the ROI and store images for training and testing dataset. Followed by developing a lightweight computational complexity CNN model for face mask recognition to identify whether the selected person's face is wearing a mask or not. The dataset contains numerous variations in appearance and viewpoint to capture different scenarios with and without mask faces. On average, the proposed face mask detection architecture realizes recall and F1 score of 98.3% and 98.31%, respectively. The training performance, on the other hand, has improved by 32.1% for training time as compared to AlexNet and 40.43% of storage space (model size) reduction compared to EfficientNet-B0. The presented framework architecture is an efficient face mask and unmask detector and can be employed as a robust medical assistant face detector in the healthcare sector for automated tracking of a patient, visitor, or staff member wearing a mask or not.

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Section: Related Workmentioning

confidence: 99%

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

Ahmed,

S. Alghareb

2024

IJCDS

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“…The authors develop a lightweight NIR FaceNet (LiNFNet) architecture and generated synthetic face images. (Liu et al, 2021) propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized on the basis of MobileFaceNet. (Wang et al, 2022) present three kinds of lightweight CNNs with different layers designed based on DenseNet's fully connected network structure.…”

Section: 60%mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.

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“…Calculate the eigenvalues of the face according to the facial feature points, and obtain a face code of a 128-dimensional feature vector. The obtained feature vector is compared with the face code in the face database, and then the similarity is compared by calculating the Euclidean distance [8] . In this way, the face with the smallest Euclidean distance within the set threshold range can be directly found.…”

Section: Region Extractionmentioning

confidence: 99%

Research on identity authentication and labeling technology based on MR neural network

Yang

Tang²

2023

International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022)

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Aiming at the problems of poor convenience, poor scalability, and low authentication rate in traditional authentication technology using physical contact authentication methods such as magnetic cards and passwords, this paper explores the accuracy and convenience of the practical application of MR neural network in personal identity authentication. In the MR wearable device, the neural network person identity authentication method is studied flexibly and quickly to detect and identify the person. The 3D information of the face is collected and preprocessed by the depth camera, and the MR identity authentication data set is established. The neural network Resnet model is used for face detection and face feature vector extraction, and the Euclidean method is used to compare the feature vectors and label the characters. The neural network authentication algorithm is mapped to the MR wearable device, and the deep face information in the scene is identified, matched, and labeled by using the unique spatial mapping of MR technology and the camera of the MR wearable device. It solves the problems of low flexibility, poor reliability of face information, and weak recognition stability in traditional identity authentication methods, enabling MR technology to provide a more intelligent identification and labeling method for person identity authentication.

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Face Recognition Based on Lightweight Convolutional Neural Networks

Cited by 19 publications

References 35 publications

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

A Hybrid ROI Extraction Approach for Mask and Unmask Facial Recognition System using Light-CNN

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Research on identity authentication and labeling technology based on MR neural network

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