Age Prediction from Sclera Images using Deep Learning

Odion, Philip O.; Musa, Md. Nor; Shuaibu, S. U.

doi:10.46481/jnsps.2022.787

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Various techniques can be used for anomaly detection, including statistical methods, clustering, nearest-neighbor methods, and machine learning algorithms such as support vector machines, decision trees, and neural networks [5]. With the increase in the volume and complexity of data, traditional rulebased approaches have proven to be insufficient in detecting anomalies.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

Fuhnwi

Agbaje

Oshinubi

et al. 2023

J. Nig. Soc. Phys. Sci.

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In data mining, and statistics, anomaly detection is the process of finding data patterns (outcomes, values, or observations) that deviate from the rest of the other observations or outcomes. Anomaly detection is heavily used in solving real-world problems in many application domains, like medicine, finance , cybersecurity, banking, networking, transportation, and military surveillance for enemy activities, but not limited to only these fields. In this paper, we present an empirical study on unsupervised anomaly detection techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), (DBSCAN++) (with uniform initialization, k-center initialization, uniform with approximate neighbor initialization, and $k$-center with approximate neighbor initialization), and $k$-means$--$ algorithms on six benchmark imbalanced data sets. Findings from our in-depth empirical study show that k-means-- is more robust than DBSCAN, and DBSCAN++, in terms of the different evaluation measures (F1-score, False alarm rate, Adjusted rand index, and Jaccard coefficient), and running time. We also observe that DBSCAN performs very well on data sets with fewer number of data points. Moreover, the results indicate that the choice of clustering algorithm can significantly impact the performance of anomaly detection and that the performance of different algorithms varies depending on the characteristics of the data. Overall, this study provides insights into the strengths and limitations of different clustering algorithms for anomaly detection and can help guide the selection of appropriate algorithms for specific applications.

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Section: Introductionmentioning

confidence: 99%

An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

Fuhnwi

Agbaje

Oshinubi

et al. 2023

J. Nig. Soc. Phys. Sci.

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Age Prediction from Facial Images Using Deep Learning Architecture

Nguyen,

Pham,

Dang

et al. 2024

Applied Computer Systems

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Predicting age and gender through images is a common computer vision problem with many practical applications. However, this problem faces many difficulties because a person’s age can be affected by genetics, living environment, diet, health, gender, and other factors. Therefore, the accuracy of the prediction model may decrease due to the enormous diversity and variability in the data. In this study, we use three models, including Unet, MobileNets, and EfficientNets, to test the performance of predicting a person’s age and gender through their photos. In addition, we also adjust the learning rate parameter to find optimal performance. The best results for gender prediction are achieved by the Unet model with the highest accuracy of 97.22 %, and the MobileNets model gives age prediction results with MAE = 2.248, learning rate 0.001 for optimal performance in the models of our study.

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A Deep Learning Approach to Predict Chronological Age

et al. 2023

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Recently, researchers have turned their focus to predicting the age of people since numerous applications depend on facial recognition approaches. In the medical field, Alzheimer’s disease mainly depends on patients’ ages. Multiple methods have been implemented and developed to predict age. However, these approaches lack accuracy because every image has unique features, such as shape, pose, and scale. In Saudi Arabia, Vision 2030, concerning the quality of life, is one of the twelve initiatives that were launched recently. The health sector has gained increasing attention as the government has introduced age-based policies to improve the health of its elderly residents. These residents are urgently advised to vaccinate against COVID-19 based on their age. In this paper, proposing a practical, consistent, and trustworthy method to predict age is presented. This method uses the color intensity of eyes and a Convolutional Neural Network (CNN) to predict age in real time based on the ensemble of CNN. A segmentation algorithm is engaged since the approach takes its input from a video stream or an image. This algorithm extracts data from one of the essential parts of the face: the eyes. This part is also informative. Several experiments have been conducted on MATLAB to verify and validate results and relative errors. A Kaggle website dataset is utilized for ages 4 to 59. This dataset includes over 270,000 images, and its size is roughly 2 GB. Consequently, the proposed approach produces ±8.69 years of Mean Square Error (MSE) for the predicted ages. Lastly, a comparative evaluation of relevant studies and the presented algorithm in terms of accuracy, MSE, and Mean Absolute Error (MAE) is also provided. This evaluation shows that the approach developed in the current study outperforms all considered performance metrics since its accuracy is 97.29%. This study found that the color intensity of eyes is highly effective in predicting age, given the high accuracy and acceptable MSE and MAE results. This indicates that it is helpful to utilize this methodology in real-life applications.

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Age Prediction from Sclera Images using Deep Learning

Cited by 3 publications

References 26 publications

An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

An Empirical Study on Anomaly Detection Using Density-based and Representative-based Clustering Algorithms

Age Prediction from Facial Images Using Deep Learning Architecture

A Deep Learning Approach to Predict Chronological Age

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