Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions
Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-5 |
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| Year of Publication : 2023 | ||
| Author : Sonia Mittal, Sanskruti Patel |
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| DOI : 10.14445/22315381/IJETT-V71I5P243 | ||
How to Cite?
Sonia Mittal, Sanskruti Patel, "Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 435-460, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P243
Abstract
Face recognition has been a central research topic in recent years. Because it has many different application areas in automatic surveillance systems, passport control, missing person detection and criminal detection, many researchers have studied the problems of face recognition and authentication for many years. However, still, many issues remain to be addressed. The limitation of this topic includes the variability in poses, facial expressions, illumination, and a large age gap between the face images. This paper presents the survey for some commonly used techniques for Age Invariant face recognition (AIFR). A comprehensive overview of the most used age-invariant methods in face recognition is presented, with a comparative overview of different approaches and a comparison in accuracy. Some facial ageing datasets, with their essential characteristics in images, no. of subjects, and age gap, are briefly presented in a tabular view. Commonly used techniques for feature extraction and classification in this field are presented. Major topics covered are experimental results obtained from these methods, issues/challenges, the scope of future work, and conclusions.
Keywords
Age invariant face recognition, Cross age face recognition, Age progression, Convolutional Neural Network, Deep learning discriminative method, Generative method.
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