A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods

Setiowati, Sulis; Zulfanahri,; Franita, Eka Legya; Ardiyanto, Igi

doi:10.1109/iciteed.2017.8250484

Cited by 31 publications

(13 citation statements)

References 11 publications

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“…Convolutional neural network (CNN) is one of the most effective neural networks that has shown its superiority in a wide range of applications, including image classification, recognition, retrieval, and object detection. CNNs typically consist of cascaded layers to control the degree of shift, scale, and distortion [49], which are input, convolutional, subsampling, fully connected, and output layers. They can efficiently learn various kinds of intra-class differences from training data, such as illumination, pose, facial expression, and age [50].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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“…Multi-class SVM for multi-class classification obtains sub-SVMs for classifying each class, and performs multi-class classification based on this idea [30,31]. Recently, deep learning models with higher classification accuracy than SVM have been proposed [32]. A representative deep learning model is the Convolutional Neural Network (CNN).…”

Section: Classificationmentioning

confidence: 99%

Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

2020

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Despite recent advances in technologies for intelligent transportation systems, the safety of intersection traffic is still threatened by traffic signal violation, called the Red Light Runner (RLR). The conventional approach to ensure the intersection safety under the threat of an RLR is to extend the length of the all-red signal when an RLR is detected. Therefore, the selection of all-red signal length is an important factor for intersection safety as well as traffic efficiency. In this paper, for better safety and efficiency of intersection traffic, we propose a framework for dynamic all-red signal control that adjusts the length of all-red signal time according to the driving characteristics of the detected RLR. In this work, we define RLRs into four different classes based on the clustering results using the Dynamic Time Wrapping (DTW) and the Hierarchical Clustering Analysis (HCA). The proposed system uses a Multi-Channel Deep Convolutional Neural Network (MC-DCNN) for online detection of RLR and also classification of RLR class. For dynamic all-red signal control, the proposed system uses a multi-level regression model to estimate the necessary all-red signal extension time more accurately and hence improves the overall intersection traffic safety as well as efficiency.

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“…One the major challenges for face recognition problem is to exctract effective features to reduce personal changes while increasing interpersonal differences. As well as, complexity and scalability of face recognition problem is also an important challenge, corresponding papers can be seen in [16,17]. WebFace [18] called CASIA WebFace dataset which contains about 10,000 subjects and 500,000 face images is built by collecting a semiautomatically from internet.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison of Deep Learning Based Architecture With a Conventional Approach for Face Recognition Problem

Ünal¹

2019

Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng.

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This paper addresses a new approach for face recognition problem based on deep learning strategy. In order to verify the performance of the proposed approach, it is compared with a conventional face recognition method by using various comprehensive datasets. The conventional approach employs Histogram of Gradient (HOG) algorithm to extract features and utilizes a multi-class Support Vector Machine (SVM) classifier to train and learn the classification. On the other hand, the proposed deep learning based approaches employ a Convolutional Neural Network (CNN) based architecture and also offer both a SVM and Softmax classifiers respectively for the classification phase. Results reveal that the proposed deep learning architecture using Softmax classifier outperform conventional method by a substantial margin. As well as, the deep learning architecture using Softmax classifier also outperform SVM in almost all cases.

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A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods

Cited by 31 publications

References 11 publications

Masked Face Recognition Using Deep Learning: A Review

Masked Face Recognition Using Deep Learning: A Review

Dynamic All-Red Signal Control Based on Deep Neural Network Considering Red Light Runner Characteristics

A Comparison of Deep Learning Based Architecture With a Conventional Approach for Face Recognition Problem

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