Age-based facial recognition using convoluted neural network deep learning algorithm

Jien, Julius Yong Wu; Baharum, Aslina; Wahab, Shaliza Hayati A.; Saad, Mohamad Naufal Mohamad; Omar, Muhamad Khalil; Noor, Noorsidi Aizuddin Mat

doi:10.11591/ijai.v9.i3.pp424-428

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…The techniques utilized in data augmentation included rotation, horizontal and vertical mirroring, zoom crop, and random adjustments to the brightness, contrast, hue, saturation, and value of the images. This technique generated similar yet slightly altered data, which positively impacted the learning ability of the neural networks and improved their accuracy levels [22].…”

Section: Datasetmentioning

confidence: 99%

Comparative study of teachable machine for forest fire and smoke detection by drone

Grari,

Yandouzi,

Mohammed

et al. 2024

Bulletin EEI

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Forests play a vital role in maintaining ecological equilibrium and serving as vital habitats for wildlife. They regulate global climate, safeguard soil and water resources, and provide crucial ecosystem services such as air and water purification, essential for human well-being and sustainable development. Forest fires wreak havoc on ecosystems and wildlife, emitting harmful pollutants, disrupting communities, and increasing the risk of erosion and landslides. Detecting forest fires through satellite imaging, aerial reconnaissance, and ground-based sensors is pivotal for early detection and containment, safeguarding human lives, wildlife, and preserving natural resources for future generations. Utilizing drones and deep learning (DL) algorithms can significantly enhance early fire detection and minimize their devastating impact. In this paper, we examine teachable machine, a Google tool for creating DL models. We compare the top model generated by teachable machine for fire and smoke detection to models obtained through transfer learning from established DL models in image recognition and computer vision (CV), such as VGG16, VGG19, MobileNet, MobileNetv2, and MobileNetv3. The results underscore the significance of employing the teachable machine model in specific fire and smoke detection scenarios.

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Section: Datasetmentioning

confidence: 99%

Comparative study of teachable machine for forest fire and smoke detection by drone

Grari,

Yandouzi,

Mohammed

et al. 2024

Bulletin EEI

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“…The global goal of AI is to provide a method to solve problems that humans perform intuitively and near automatically. AI-related to work in inference, planning, heuristics, or automatic machine reasoning [1]- [4]. Machine learning (ML) is a subfield of AI technology.…”

Section: Introductionmentioning

confidence: 99%

Labeling of an intra-class variation object in deep learning classification

Santiary

Swardika

Purnama

et al. 2022

IJ-AI

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Machine orientation learning had demonstrated that deep learning (DL)-convolutional neural networks (CNNs) were robust image classifiers with significant accuracy. Although to been functional, DL scope classification as tight, well-defined as possible uses a 2-class object, for instance, cats and dogs. The DL classification faced many challenges, e.g., variation factors, the intra-class variation. This nature is presented in every object, its diversity of an object. The label was an exact given name of an intra-class variation object. Unfortunately, not every object had a specific name, in exceptionally high similarity inside the category. This paper explored those problems in flower plants’ taxonomy naming. In supervised learned of DL, image datasets musted labeled with a meaningful word or phrase that humans are familiar with, a taxonomy naming. Labeled with visual feature extraction brought a fully automatic classification. Flower Plumeria L labeling extracted from perspective dimension scale of petal flower which automatically obtained by contour detection, and peaks of blue green red (BGR) histogram channels from bins histogram after object masked. Dataset collected on photography workbench equipped with webcam and ring light. Results showed labels for intra-class variation of Plumeria L in form of dimension-scale and BGR-peaks. The result of this study presented a novelty in building datasets for intra-class variation for the DL classification.

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“…According to [2] there are several filters like average, median, gaussian, and bilateral are used to smooth the image. In this situation the convolution is used and is represented by operator ⊛ applied on 𝑓(𝑥, 𝑦) with the impulse response of 𝑔(𝑥, 𝑦) create smooth image ℎ(𝑥, 𝑦) explained as (3).…”

Section: Introductionmentioning

confidence: 99%

“…The human face represents some sensible information which changes from time to time [3] with external or internal influence. In this article we have demonstrated the facial emotion recognition model by applying artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

Mishra¹,

Srinivas²

2021

IJ-AI

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The facial emotion recognition by the machine is a challenging task. From decades, researchers applied different methods to classify facial emotion into the different classes. The expansion of artificial intelligence in a form of deep convolutional neural network (CNN) changed the direction of the research. The facial emotion recognition using deep CNN is powerful in terms of taking bulk input images for processing and classify with high accuracy. It has been noticed in a few cases the classification model does not judge the facial images into appropriate classes due to the influence of noises. So, it is highly recommended to apply a noiseless image to the facial emotion recognition model for classification. We adopted a mechanism and proposed a model for classifying facial image into one of the seven classes with high accuracy. The images are smoothed before applying to the model by different smoothing process as part of image preprocessing. We claim facial emotion recognition with image smoothing by different filters or a mixture of filter are more robust than without preprocessing. The detail is explained in the subsequent sections.

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Age-based facial recognition using convoluted neural network deep learning algorithm

Cited by 5 publications

References 15 publications

Comparative study of teachable machine for forest fire and smoke detection by drone

Comparative study of teachable machine for forest fire and smoke detection by drone

Labeling of an intra-class variation object in deep learning classification

Facial emotion recognition using deep convolutional neural network and smoothing, mixture filters applied during preprocessing stage

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