Illumination invariant face recognition using Fused Cross Lattice Pattern of Phase Congruency (FCLPPC)

Koley, Subhadeep; Roy, Hiranmoy; Dhar, Soumyadip; Bhattacharjee, Debotosh

doi:10.1016/j.ins.2021.10.059

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…Although this new approach for combining palm printing was used the scientists felt it could deliver accurate results for venous patterns. Koley et al [22] addresses the issue of users' authentication of biometric facials. Proposed authentication neural network model based on a two-layered perceptron with input neurons 90, hidden neurons 10 and output neurons 4.…”

Section: Related Workmentioning

confidence: 99%

A hybrid facial features extraction-based classification framework for typhlotic people

Chopparapu,

Seventline Joseph

2024

Bulletin EEI

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Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.

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Section: Related Workmentioning

confidence: 99%

A hybrid facial features extraction-based classification framework for typhlotic people

Chopparapu,

Seventline Joseph

2024

Bulletin EEI

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“…Face recognition systems have been widely used in a variety of applications, including visual surveillance (Zhang et al, 2018), automated border control (del Rio et al, 2016), education systems (Jadhav et al, 2021) and healthcare (Bargshady et al, 2020). Face recognition technology needs to be more efficient while confronting obstacles such as varying illumination (Koley et al, 2022), low resolution (Zangeneh et al, 2020), different pose (Thai et al, 2022), expression change (Huang et al, 2021) and occlusion (Long et al, 2018; Peng et al, 2023; Zeng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

ABANet: Attention boundary‐aware network for image segmentation

Rezvani,

Fateh,

Khosravi

2024

Expert Systems

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Deep learning techniques have attained substantial progress in various face‐related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID‐19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary‐aware attention network combined with a new hybrid loss to provide a map, patch, and pixel‐level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting‐edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state‐of‐the‐art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% F1‐score rate. Our dataset of masked faces with mask region labels and source code will be available online.

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“…Phase congruency is another technique used for edge detection, although it is less well-known. Despite several recent works utilizing this method, such as its integration into pipelines for image registration or edge detection in various applications [11][12][13], there have been no new developments concerning the principles of this technique, except those produced by the authors themselves [14].…”

Section: Introductionmentioning

confidence: 99%

Generalized Quantification Function of Monogenic Phase Congruency

Forero,

Jacanamejoy,

Machado

et al. 2023

Mathematics

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Edge detection is a technique in digital image processing that detects the contours of objects based on changes in brightness. Edges can be used to determine the size, orientation, and properties of the object of interest within an image. There are different techniques employed for edge detection, one of them being phase congruency, a recently developed but still relatively unknown technique due to its mathematical and computational complexity compared to more popular methods. Additionally, it requires the adjustment of a greater number of parameters than traditional techniques. Recently, a unique formulation was proposed for the mathematical description of phase congruency, leading to a better understanding of the technique. This formulation consists of three factors, including a quantification function, which, depending on its characteristics, allows for improved edge detection. However, a detailed study of the characteristics had not been conducted. Therefore, this article proposes the development of a generalized function for quantifying phase congruency, based on the family of functions that, according to a previous study, yielded the best results in edge detection.

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Illumination invariant face recognition using Fused Cross Lattice Pattern of Phase Congruency (FCLPPC)

Cited by 14 publications

References 42 publications

A hybrid facial features extraction-based classification framework for typhlotic people

A hybrid facial features extraction-based classification framework for typhlotic people

ABANet: Attention boundary‐aware network for image segmentation

Generalized Quantification Function of Monogenic Phase Congruency

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