Face Recognition using Fuzzy Logic

Basak, Suchitra; Jia, Ruting; Lei, Chengwei

doi:10.1109/icinfa.2018.8812593

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The image without a front face could not be recognized, which was the only problem with this algorithm. Suchitra Basak et.al [17] proffered the usage of Fuzzy Logic for the same task. In an unrestrained environment, the images in the database were taken.…”

Section: Literature Surveymentioning

confidence: 99%

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

Thapliyal¹,

Verma²,

Kumar³

2022

IJETAE

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Right from the beginning of the COVID-19 outbreak, everyone is aware of the havoc caused by the pandemic. To curb its spread, every healthcare agency and civic body around the globe has been advising to wear masks. However, this necessary practice has posed a significant challenge for the modern-day Facial Recognition technology. Face recognition finds significant application in the security domain that demands speed and accuracy both simultaneously. This requires the system to be highly optimized and efficient. Through this paper, we present a novel approach using Haar cascade classifier for face detection with Local Binary Patterns Histograms (LBPH) face recognizer. This work further goes on to address the various problems that occur when the user wears a mask that covers a different area and percentage coverage of the face resulting in inaccuracies as various tests come with false negatives or false positives. This problem is addressed by making use of a fuzzybased system that decides the “threshold confidence score” needed to pass the authentication dynamically. Our proposed model for masked Face recognition achieves an accuracy of 86% when a Haar-feature-based cascade classifier with LBPH face recognizer is used standalone which further improves to around 97% when used in conjunction with a fuzzy system

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Section: Literature Surveymentioning

confidence: 99%

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

Thapliyal¹,

Verma²,

Kumar³

2022

IJETAE

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“…This allows us to take into account any situation's inaccuracies and uncertainties. It's a tool for integrating human-like thought into a computer control system [29]. It isn't designed to be precise, but it is designed to be appropriate.…”

Section: Neuro Fuzzy Classificationmentioning

confidence: 99%

A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

Raj¹

2021

TURCOMAT

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Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

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“…It is broadly used in controllers where it is hard to obtain a mathematical model, or systems that need to work with some vagueness or ambiguousness. Some applications created with fuzzy logic would be medical applications [21], daily life applications as the washing machine [22], natural interaction with face recognition [23] and posture recognition [24] and lastly smart cities. Perhaps the last one could be disconcerting, notwithstanding the growth of cities has generated various problems in them.…”

Section: B Fuzzy Logicmentioning

confidence: 99%

Ithaca. A Tool for Integrating Fuzzy Logic in Unity

Moreno

Piedra-Fernández

Ojeda-Castelo

et al. 2021

2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI)

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Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.

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Face Recognition using Fuzzy Logic

Cited by 3 publications

References 2 publications

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

Ithaca. A Tool for Integrating Fuzzy Logic in Unity

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