Age Group Estimation from Human Iris

Rajput, Minakshi R.; Sable, Ganesh S.

doi:10.1007/978-981-15-2475-2_48

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Multi-task Fusion CNN [35] OU-ISIR [69] (389 male and 355 female) 97.70% Multi-view PA-GCR+GaitSet [70] OU-MVLP [71] (5114 male and 5193 female) 94.27% DCT+XGBoost [72] OU-MVLP [71] (5114 male and 5193 female) 95.33% Fingerprint DWT+KNN [73] Self-created (2830 fingerprints within 74.54% 13-35 years, divided into three groups) DFT+STFT+LBP+LPQ+KNN [28] Self-created (494 users separated into two groups 89.10% of 283 young and 211 elderly people) Iris DWT Stats+Ensemble DT [74] Self-created (213 subjects with age ranging 83.70% from 2-75 years, separated into three groups) PDM+Adaboost and SVM Fusion [32] GANT [75] (112 subjects with ages range 91.11% between 17-80 years, separated into seven groups) Face ASM+CNN [54] Adience [76] (2284 subjects labeled by 92.62% eight age groups) FG-NET [77] (82 people with ages range 94.59% 0-69 years, divided into four age groups) Pretrained VGG16 [34] Adience [76] (2284 subjects labeled by 96.60% eight age groups) Gated Residual Attention Network [55] UTKFace [65] (20K face images ranging 93.70% 0-116 years, divided into ten age categories) Gait Multi-task Fusion CNN [35] OU-ISIR [69] (744 subjects with ages range 96.10% 2-78 years, divided into six age groups) Pyramid GEIs+Parallel Fused CNN [56] OULP-Age [78] (63,846 subjects with ages range 93.50% 2-90 years, divided into five age groups) Joint Angles Fourier series+KNN [36] USF (initial) [79] (74 subjects with ages range 98.20% 19-54 years, divided into four age groups)…”

Section: Gaitmentioning

confidence: 99%

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Zabala-Blanco,

Hernández-García,

Barrientos

2023

Electronics

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Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

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Section: Gaitmentioning

confidence: 99%

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Zabala-Blanco,

Hernández-García,

Barrientos

2023

Electronics

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“…They achieved a 75% accuracy rate. Again, [24] proposed a technique that used the iris structure to estimate a person's age group. The image input was obtained from an iris database and was divided into three age groups.…”

Section: Literature Reviewmentioning

confidence: 99%

Age Prediction from Sclera Images using Deep Learning

Odion

Musa

Shuaibu

2022

J. Nig. Soc. Phys. Sci.

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Automatic age classification has drawn the interest of many scholars in the fields of machine learning and deep learning. In this study, we looked at the problem of estimating age groups using different biometric modalities of human beings. We looked at the problem of determining age groups in humans using various biometric modalities. Specifically, we focused on the use of transfer learning for sclera age group classification. 2000 Sclera images were collected from 250 individuals of various ages, and Otsu thresholding was used to segment the images using morphological processes. Experiment was conducted to determine how accurately the age group of a person can be classified from sclera images using pretrained CNN architectures. The segmented images were trained and tested on four different pre-trained models (VGG16, ResNet50, MobileNetV2, EffcientNet-B1), which were compared based on different performance metrics in which ResNet-50 was shown to outperform the others, resulting in an accuracy, precision, recall and F1-score of 95% while VGG-16, EffcientNetB1, and MobileNetV2 had 94%, 93%, and 91%, respectively. The findings from the study showed that there is an aging template in the sclera that can be utilized to classify age.

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“…In recent years, significant progress has been made in improving identity recognition accuracy by studying each attribute separately. For instance, researchers have classified iris images based on age and gender in [6][7][8]. However, age-related research in handwriting recognition still presents challenges.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

Zhao,

Wu,

Chen

2024

ijacsa

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Currently, age estimation is a hot research topic in the field of forensic biology. Age estimation methods based on facial or brain features are easily affected by external factors. In contrast, handwriting analysis is a more reliable method for age estimation. This paper aims to improve the accuracy and efficiency of age prediction using handwriting analysis by proposing a novel method that integrates a coordinate attention mechanism in a deep residual network (CA-ResNet). This method can more accurately capture important features in the input handwritten images while reducing the number of model parameters, thereby improving the accuracy (Acc) and efficiency of the model for age estimation. The proposed method is evaluated on standard handwriting datasets and the created dataset, and it is compared with the current state-of-the-art methods. The results show that the method consistently outperforms others, achieving an accuracy of 79.60% on the IAM handwriting dataset, with a 6.31% improvement over other methods.

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Age Group Estimation from Human Iris

Cited by 4 publications

References 17 publications

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Age Prediction from Sclera Images using Deep Learning

Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms

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