An Efficient Convolutional Neural Network Approach for Facial Recognition

Mangal, Aayushi; Malik, Himanshu; Aggarwal, Garima

doi:10.1109/confluence47617.2020.9058109

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…It is important to notice that the second most largest category goes to private datasets with 34 papers (Zhou et al, 2018;Ara et al, 2017;Phankokkruad, 2018;Gilani and Mian, 2018;Khan et al, 2019b;Qin et al, 2019;Liu et al, 2019;Peng et al, 2019;Mangal et al, 2020;Lv et al, 2020;Perti et al, 2020;Kim et al, 2017;Irjanto and Surantha, 2020;Arafah et al, 2020;Prasetyo et al, 2021;Moon et al, 2017;Chandran et al, 2018;Yang et al, 2018;Son et al, 2020;Alhanaee et al, 2021;Khan et al, 2020;Nakajima et al, 2021;Talahua et al, 2021;He and Ding, 2023;Karlupia et al, 2023;Bussey et al, 2017;Li et al, 2022;Filippidou and Papakostas, 2020;Bussey et al, 2017;Singh et al, 2022;Setio Aji et al, 2022;Wang et al, 2022;Lestari et al, 2021) creating their own datasets for testing. There are advantages and disadvantages to developing and utilizing private face image datasets for CNN-based face recognition.…”

Section: Assessment Of Q3: What Type Of Cnn Model Is Most Commonly Us...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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Section: Assessment Of Q3: What Type Of Cnn Model Is Most Commonly Us...mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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“…One of the factors that contributes to its popularity is the extensive use of surveillance cameras in various applications [ 9 ]. In the past two decades, numerous face recognition methods have been developed to recognize a person for various purposes, such as criminal detection, law enforcement, image spoofing, and other security applications [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The pioneer of face recognition utilizes either the visible light images or infrared images to identify a person [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…The recognition of these two types of images is done in the same spectral band. In addition, some efforts to apply deep learning in face image recognition have been demonstrated in [ 14 , 15 , 16 , 17 ]. These works also considered the recognition between images in the same spectral band, i.e., the visible images and their various versions.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, facial images used for the identification purpose are captured at different electromagnetic spectra and wavelengths, namely, Near-Infrared (NIR), Visible Light (VIS), Short Wavelength Spectrum (SWIR), and Long Wavelength Infrared (LWIR) [ 14 ]. A face recognition system using different spectra, such as VIS and NIR, referred to as cross-spectral (CS) face recognition, is becoming more attractive because of the benefits it potentially brings in the security area and surveillance applications, such as helping to identify a criminal [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Arnia

Oktiana

Saddami

et al. 2021

Sensors

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Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

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“…Some of the multiple applications in which CNN can be implemented are the recognition and classification of faces [8], [9], recognition of traffic signals [10], [11], and visual analysis of written documents [12]. However, the CNN focuses on classifying the image within a single category, given that they were trained in that way, with a category by image, with multiples training for different databases such as MNIST, NORB, NIST SD 19 [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Tool Sorting Algorithm Using Faster R-CNN and Haar Classifiers

Moreno¹,

Useche²,

Pinzón-Arenas³

2021

International Journal on Advanced Science, Engineering and Information Technology

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The following paper presents an algorithm for sorting up to 5 different tools based on deep learning and specifically in a convolutional neural network, according to the top in pattern recognition found in state of the art and compared by a Haar classifier in object recognition tasks. A Faster R-CNN is used to detect and classify tools located randomly on a table and a Haar classifier to detect other tools delivered by the user. The Faster R-CNN allows recognizing the existing tools on the table and where they are located in the physical space. The Haar classifier detects and tracks, in real-time, a tool delivered by the user's hand to sort it on the table, together with the other elements. Both the training of the convolutional network and the design of the Haar classifier are exposed. The algorithm detects and classifies the tools found on a table, then orders them side by side, and finally waits for the user to deliver some of the five missing tools on the table, take it from his hand, and locate it at the end of the row of objects. A Faster R-CNN was used with an accuracy of 70.8% and a Haar classifier with a 96% recognition, managing to order the five tools in a physical environment. The average time in comparison demonstrates that the Haar classifier presents a lower computational cost.

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An Efficient Convolutional Neural Network Approach for Facial Recognition

Cited by 3 publications

References 14 publications

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Tool Sorting Algorithm Using Faster R-CNN and Haar Classifiers

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