Facial recognition and 3D non-rigid registration

Makovetskii, Artyom; Kober, Vitaly; Voronin, Aleksei; Zhernov, Dmitrii

doi:10.1109/itnt49337.2020.9253224

Cited by 3 publications

(2 citation statements)

References 18 publications

(3 reference statements)

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“…(Gilani and Mian, 2018) propose the first deep CNN model designed specifically for 3D face recognition as well as a method for generating a large number of labeled 3D face identities. (Makovetskii et al, 2020) propose a resampling method of human faces represented by 3D point clouds. The method is based on a non-rigid Iterative Closest Point (ICP) algorithm and a CNN.…”

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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Section: 60%mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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“…Regarding the face of a human as a biometric, the related tasks can be face detection [62], [63], [64], [65], face alignment [66], [67], face recognition [68], face tracking [69], [70], face classification/verification [71], and face landmarks extraction [72], [73], [74]. Fingerprint [75], [76], [77], palmprint [78] and iris/gaze [79], [80] are mainly used for user's identification tasks due to their uniqueness for each person.…”

Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

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Cyber Physical Systems have been going into a transition phase from individual systems to a collecttives of systems that collaborate in order to achieve a highly complex cause, realizing a system of systems approach. The automotive domain has been making a transition to the system of system approach aiming to provide a series of emergent functionality like traffic management, collaborative car fleet management or large-scale automotive adaptation to physical environment thus providing significant environmental benefits (e.g air pollution reduction) and achieving significant societal impact. Similarly, large infrastructure domains, are evolving into global, highly integrated cyber-physical systems of systems covering all parts of the value chain. In practice, there are significant challenges in CPSoS applicability and usability to be addressed, i.e. even a small CPSoS such as a car consists several subsystems Decentralization of CPSoS appoints tasks to individual CPSs within the System of Systems. CPSoSs are heterogenous systems. They comprise of various, autonomous, CPSs, each one of them having unique performance capabilities, criticality level, priorities and pursued goals. all CPSs must also harmonically pursue systembased achievements and collaborate in order to make system-ofsystem based decisions and implement the CPSoS functionality. This survey will provide a comprehensive review on current best practices in connected cyberphysical systems. The basis of our investigation is a dual layer architecture encompassing a perception layer and a behavioral layer. Perception algorithms with respect to scene understanding (object detection and tracking, pose estimation), localization mapping and path planning are thoroughly investigated. Behavioural part focuses on decision making and human in the loop control.

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