Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19

Zeebaree, Imad Majed; Kareem, Omer Sedqi

doi:10.59543/ijmscs.v2i.7845

ijmscs

2023

DOI: 10.59543/ijmscs.v2i.7845

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Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19

Imad Majed Zeebaree,

Omer Sedqi Kareem

Abstract: The global epidemic causes extensive damage. Several sectors, including tourism and recreation, have been halted. Since these projects require work together in close contact, it increases the danger of infection. To reduce the spread of COVID-19, keep a safe distance from others and always wear protective clothing. In light of this, we have developed COVID Vision, a system that uses Haar cascades as a classifier for a face mask detector. This will reduce reliance on employees while maintaining COVID-19 criteri… Show more

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Cited by 4 publications

References 13 publications

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An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

Gunasekaran,

Ambeth Kumar,

Jayashree

2024

Computer Methods in Biomechanics and Biomedical Engineering

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An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

Gunasekaran,

Ambeth Kumar,

Jayashree

2024

Computer Methods in Biomechanics and Biomedical Engineering

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KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition

Oinar,

M. Le,

Woo

2023

IEEE Access

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Feature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and inter-class diversity, without considering imbalanced learning issues that arise due to different learning difficulties or the number of training samples available in each class. This overlook not only compromises the efficiency of the learning process but, more critically, the generalization capability of the resultant models. To address this problem, we introduce a novel adaptive strategy called KappaFace, which modulates the relative importance of each class based on its learning difficulty and imbalance. Drawing inspiration from the von Mises-Fisher distribution, KappaFace increases the margin values for the challenging or underrepresented classes and decreases that of more well-represented classes. Comprehensive experiments across eight cutting-edge baselines and nine well-established facial benchmark datasets strongly confirm the advantage of our method. Notably, we observed an enhancement of up to 0.5% on the verification task when evaluated on the IJB-B/C datasets. In conclusion, KappaFace offers a novel solution that effectively tackles imbalanced learning in deep face recognition tasks and establishes a new baseline.

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Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

Aziz,

Sztahó

2024

Expert Systems

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Machine Learning (ML) algorithms have demonstrated remarkable performance in dysphonia detection using speech samples. However, their efficacy often diminishes when tested on languages different from the training data, raising questions about their suitability in clinical settings. This study aims to develop a robust method for cross‐ and multi‐lingual dysphonia detection that overcomes the limitation of language dependency in existing ML methods. We propose an innovative approach that leverages speech embeddings from speaker verification models, especially ECAPA and x‐vector and employs a majority voting ensemble classifier. We utilize speech features extracted from ECAPA and x‐vector embeddings to train three distinct classifiers. The significant advantage of these embedding models lies in their capability to capture speaker characteristics in a language‐independent manner, forming fixed‐dimensional feature spaces. Additionally, we investigate the impact of generating synthetic data within the embedding feature space using the Synthetic Minority Oversampling Technique (SMOTE). Our experimental results unveil the effectiveness of the proposed method for dysphonia detection. Compared to results obtained from x‐vector embeddings, ECAPA consistently demonstrates superior performance in distinguishing between healthy and dysphonic speech, achieving accuracy values of 93.33% and 96.55% in both cross‐lingual and multi‐lingual scenarios, respectively. This highlights the remarkable capabilities of speaker verification models, especially ECAPA, in capturing language‐independent features that enhance overall detection performance. The proposed method effectively addresses the challenges of language dependency in dysphonia detection. ECAPA embeddings, combined with majority voting ensemble classifiers, show significant potential for improving the accuracy and reliability of dysphonia detection in cross‐ and multi‐lingual scenarios.

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Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19

Cited by 4 publications

References 13 publications

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition

Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

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