Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications

Ṣiddiqi, Muḥammad Zubair; Khan, Khalil; Khan, Rehan Ullah; Alsirhani, Amjad

doi:10.3390/electronics11081210

Cited by 6 publications

(2 citation statements)

References 169 publications

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“…Recently, with the advancement of digital image processing and computer vision methodologies especially using machine learning and deep learning, researchers have applied these techniques in different medical fields [ 47 ] and in particular to OSA detection during wakefulness using facial landmarks [ 47 , 48 ]. It is well known that people with craniofacial abnormalities are often diagnosed with OSA [ 48 – 50 ], based on that OSA detection might be possible by using craniofacial characteristics using landmarks from different image postures [ 51 ]. Figure 6 shows a standard landmark system used for OSA detection using facial image analysis.…”

Section: Methodsmentioning

confidence: 99%

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Alqudah,

Elwali,

Kupiak

et al. 2024

Med Biol Eng Comput

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Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs. Graphical abstract

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Section: Methodsmentioning

confidence: 99%

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Alqudah,

Elwali,

Kupiak

et al. 2024

Med Biol Eng Comput

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“…With the continuous evolution of deep learning, classic models like LeNet-5 [27], AlexNet [28], VGG [29], ResNet [30], MobileNet [31], and EfficientNet [32] have been introduced. These models have been applied in various domains such as image classification [33,34], object detection [35], semantic segmentation [36], facial recognition [37], medical image analysis [38], autonomous driving [39], and natural language processing [40], achieving outstanding results.…”

Section: Research On Machine Learning For Architectural Classificationmentioning

confidence: 99%

Classification and Model Explanation of Traditional Dwellings Based on Improved Swin Transformer

Miao,

Zhang,

Piao

et al. 2024

Buildings

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The extraction of features and classification of traditional dwellings plays significant roles in preserving and ensuring the sustainable development of these structures. Currently, challenges persist in subjective classification and the accuracy of feature extraction. This study focuses on traditional dwellings in Gansu Province, China, employing a novel model named Improved Swin Transformer. This model, based on the Swin Transformer and parallel grouped Convolutional Neural Networks (CNN) branches, aims to enhance the accuracy of feature extraction and classification precision. Furthermore, to validate the accuracy of feature extraction during the prediction process and foster trust in AI systems, explainability research was conducted using Grad-CAM-generated heatmaps. Initially, the Gansu Province Traditional Dwelling Dataset (GTDD) is established. On the constructed GTDD dataset, the Improved Swin Transformer attains an accuracy of 90.03% and an F1 score of 87.44%. Comparative analysis with ResNet-50, ResNeXt-50, and Swin Transformer highlights the outstanding performance of the improved model. The confusion matrix of the Improved Swin Transformer model reveals the classification results across different regions, indicating that the primary influencing factors are attributed to terrain, climate, and cultural aspects. Finally, using Grad-CAM-generated heatmaps for explaining classifications, it is observed that the Improved Swin Transformer model exhibits more accurate localization and focuses on features compared to the other three models. The model demonstrates exceptional feature extraction ability with minimal influence from the surrounding environment. Simultaneously, through the heatmaps generated by the Improved Swin Transformer for traditional residential areas in five regions of Gansu, it is evident that the model accurately extracts architectural features such as roofs, facades, materials, windows, etc. This validates the consistency of features extracted by the Improved Swin Transformer with traditional methods and enhances trust in the model and decision-making. In summary, the Improved Swin Transformer demonstrates outstanding feature extraction ability and accurate classification, providing valuable insights for the protection and style control of traditional residential areas.

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Improvement of Facial Beauty Prediction Using Artificial Human Faces Generated by Generative Adversarial Network

2023

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Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications

Cited by 6 publications

References 169 publications

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review

Classification and Model Explanation of Traditional Dwellings Based on Improved Swin Transformer

Improvement of Facial Beauty Prediction Using Artificial Human Faces Generated by Generative Adversarial Network

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