Alice Othmani, Abdul Rahman Taleb, Hazem Abdelkawy, Abdenour Hadid, Age estimation from faces using deep learning: A comparative analysis, Computer Vision and Image Understanding, Volume 196, 2020, 102961, ISSN 1077-3142, https://doi.org/10.1016/j.cviu.2020.102961
Age estimation from faces using deep learning : a comparative analysis
|Author:||Othmani, Alice1; Taleb, Abdul Rahman2; Abdelkawy, Hazem1;|
1Université Paris-Est, LISSI, UPEC, 94400, Vitry sur Seine, France
2Sorbonne Université, 75006 Paris, France
3Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020112092126
|Publish Date:|| 2022-04-03
Automatic Age Estimation (AAE) has attracted attention due to the wide variety of possible applications. However, it is a challenging task because of the large variation of facial appearance and several other extrinsic and intrinsic factors. Most of the proposed approaches in the literature use hand-crafted features to encode ageing patterns. Deeply learned features extracted by Convolutional Neural Networks (CNNs) algorithms usually perform better than hand-crafted features. The main contribution of this paper is an extensive comparative analysis of several frameworks for real AAE based on deep learning architectures. Different well-known CNN architectures are considered and their performances are compared. MORPH, FG-NET, FACES, PubFig and CASIA-web Face datasets are used in our experiments. The robustness of the best deep estimator is evaluated under noise, expression changes, “crossing” ethnicity and “crossing” gender. The experimental results demonstrate the high performances of the popular CNNs frameworks against the state-of-art methods of automatic age estimation. A Layer-wise transfer learning evaluation is done to study the optimal number of layers to fine-tune on AAE task. An evaluation framework of Knowledge transfer from face recognition task across AAE is performed. We have made our best-performing CNNs models publicly available that would allow one to duplicate the results and for further research on the use of CNNs for AAE from face images.
Computer vision and image understanding
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
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