Evaluation of Classification and Regression Models Using Facial Images for Human Age Estimation

Authors

  • Bhuvaneshwari Patil, Shivanand Ruma, Mallikarjun Hangarge

Keywords:

Convolutional Neural Network, Regression, Classification

Abstract

The growing interest in automatic age prediction from facial images stems from its security control, potential applications in law enforcement and Human-Computer Interaction (HCI). However, despite notable advancements in this field, automatic age estimation remains a formidable challenge. This complexity arises because the face aging process is influenced not only by intrinsic factors such as genetic components but also by extrinsic factors like lifestyle, expressions, and environmental conditions. Consequently, individuals of the same age may exhibit markedly different appearances due to varying rates of facial aging. In response to these challenges, we propose an experimental approach for automatic age estimation.In literature, researchers carried their research work using pre-trained models as these models saves time, cost and computational resources, avoids over-fitting with increased accuracy  but  these models are domain specific and acts as black box which makes fine-tuning  difficult. In our research, we designed a Convolution Neural Networks model for age estimation and analyzed the performance on the publicly available FGNET, Adience, APPA-REAL, UTKFace, and All-Age-Face datasets by considering age estimation as classification and regression problem. Then compared the proposed model performance with the existing pre-trained models and observed both classification and regression results are in par with the performance of the existing pre-trained models.

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References

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Published

26.03.2024

How to Cite

Bhuvaneshwari Patil. (2024). Evaluation of Classification and Regression Models Using Facial Images for Human Age Estimation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3875–3884. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6159

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Section

Research Article