Comprehensive review on facial based human age estimation

Shejul, Anjali A.; Kinage, Kishor S.; Reddy, B. Eswara

doi:10.1109/icecds.2017.8390049

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Automated age estimation has been an actively researched topic in recent years, as detailed in a number of comparative surveys [3], [4], [6], [7], [8], [9], [10], [11]. While earlier works [1], [5] predominately focused on explicitly modeling the aging process using various computer vision techniques and hand-crafted features, current age estimation methods typically apply some form of deep learning.…”

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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“…The problem of estimating the age of human via a face image is deemed as a very active research issue in the last decades, due to the need to provide and develop the property or ability to determine the age of individuals from their faces (see Fig. 1) in many real-world applications, for example forensics, crime detection and prevention, identification of persons missing for several years, surveillance systems for suspects discovery, facial recognition and verification, access control for web content based on age, age simulation, cosmetics surgery, biometrics, and many other fields [2,3].…”

Section: Introductionmentioning

confidence: 99%

Semantic Human Face Analysis for Multi-level Age Estimation

Howyan¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.

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