Facial age estimation using tensor based subspace learning and deep random forests

Guehairia, O.; Dornaika, Fadi; Ouamane, Abdelmalik; Taleb‐Ahmed, Abdelmalik

doi:10.1016/j.ins.2022.07.135

Cited by 14 publications

(3 citation statements)

References 30 publications

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“…They approach the age estimation problem as a combination of classification and regression. Guehairia et al [23] propose a complex pipeline consisting of feature extraction using pretrained models for facial age estimation followed by a series of transformations in feature space for dimensionality reduction. They use deep random forest classifier to obtain final age estimation.…”

Section: Related Workmentioning

confidence: 99%

Facial Age Estimation Models for Embedded Systems: A Comparative Study

et al. 2023

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Automated age estimation from face images is the process of assigning either an exact age or a specific age range to a facial image. In this paper a comparative study of the current techniques suitable for this task is performed, with an emphasis on lightweight models suitable for embedded implementation. We investigate both the suitable modern deep learning architectures for feature extraction and the variants of framing the problem itself as either classification, regression or soft label classification. The models are evaluated on Audience dataset for age group classification and FG-NET dataset for exact age estimation. To gather in-depth insights into automated age estimation and in contrast to existing studies, we additionally compare the performance of both classification and regression on the same dataset. We propose a novel loss function that combines regression and classification approaches and show that it outperforms other considered approaches. At the same time, with a lightweight backbone, such an architecture is suitable for implementation on embedded devices.

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Section: Related Workmentioning

confidence: 99%

Facial Age Estimation Models for Embedded Systems: A Comparative Study

et al. 2023

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“…These advancements have helped overcome the challenges associated with facial beauty prediction, such as the lack of resources and the subjective nature of beauty [13,14]. These advancements in deep learning and computer vision have allowed for the development of more robust and reliable prediction models, which can be applied to various industries, including cosmetics, fashion, and entertainment [15,16]. Furthermore, the power and versatility of these algorithms, particularly Convolutional Neural Networks (CNNs), have fueled the development of deep learning architecture [17].…”

Section: Introductionmentioning

confidence: 99%

Facial Beauty Prediction Based on Vision Transformer

Boukhari

2024

IJEETC

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Facial beauty analysis is a crucial subject in human culture among researchers across various applications. Recent studies have utilized multidisciplinary approaches to examine the relationship between facial traits, age, emotions, and other factors. Facial beauty prediction is a significant visual recognition challenge that evaluates facial attractiveness for human perception. This task demands considerable effort due to the novelty of the field and the limited resources available, including a small database for facial beauty prediction. In this context, a deep learning method has recently shown remarkable capabilities in predicting facial beauty. Additionally, vision Transformers have recently been introduced as novel deep learning approaches and have shown strong performance in various applications. The key issue is that the vision transformer performs significantly worse than ResNet when trained on a small ImageNet database. In this paper, we propose to address the challenges of predicting facial beauty by utilizing vision transformers instead of relying on feature extraction based on Convolutional Neural Networks, which are commonly used in traditional methods. Moreover, we define and optimize a set of hyperparameters according to the SCUT-FBP5500 benchmark dataset. The model achieves a Pearson coefficient of 0.9534. Experimental results indicated that using this proposed network leads to better predicting facial beauty closer to human evaluation than conventional technology that provides facial beauty assessment.

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“…However, pattern recognition is a challenging task because it is not easy to locate a rhythm from the noisy data. However, several algorithms are being applied in pattern recognition, which include clustering, machine learning, deep learning, multi-linear subspace learning, and deep learning [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

et al. 2022

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Pattern recognition plays a critical role in integrative bioinformatics to determine the structural patterns of proteins of viruses such as SARS-CoV-2. This study identifies the pattern of SARS-CoV-2 proteins to depict the structure–function relationships of the protein alphabets of SARS-CoV-2 and COVID-19. The assembly enumeration algorithm, Anisotropic Network Model, Gaussian Network Model, Markovian Stochastic Model, and image comparison protein-like alphabets were used. The distance score was the lowest with 22 for “I” and highest with 40 for “9”. For post-processing and decision, two protein alphabets “C” (PDB ID: 6XC3) and “S” (PDB ID: 7OYG) were evaluated to understand the structural, functional, and evolutionary relationships, and we found uniqueness in the functionality of proteins. Here, models were constructed using “SARS-CoV-2 proteins” (12 numbers) and “non-SARS-CoV-2 proteins” (14 numbers) to create two words, “SARS-CoV-2” and “COVID-19”. Similarly, we developed two slogans: “Vaccinate the world against COVID-19” and “Say no to SARS-CoV-2”, which were made with the proteins structure. It might generate vaccine-related interest to broad reader categories. Finally, the evolutionary process appears to enhance the protein structure smoothly to provide suitable functionality shaped by natural selection.

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Facial age estimation using tensor based subspace learning and deep random forests

Cited by 14 publications

References 30 publications

Facial Age Estimation Models for Embedded Systems: A Comparative Study

Facial Age Estimation Models for Embedded Systems: A Comparative Study

Facial Beauty Prediction Based on Vision Transformer

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

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