Cross-Dataset Learning for Age Estimation
Age estimation from a single human face image has been an important yet challenging task in computer vision and multimedia. Due to the large individual differences in human faces, including the differences in races and genders, the performance of a learning model depends largely on training data. Th...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9721298/ |
| _version_ | 1819105208374919168 |
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| author | Beichen Zhang Yue Bao |
| author_facet | Beichen Zhang Yue Bao |
| author_sort | Beichen Zhang |
| collection | DOAJ |
| description | Age estimation from a single human face image has been an important yet challenging task in computer vision and multimedia. Due to the large individual differences in human faces, including the differences in races and genders, the performance of a learning model depends largely on training data. The existing learning methods are challenged by insufficient numbers of images and poor-quality images in datasets, as well as by new low-precision data that are dissimilar to existing training data. In this paper, we propose a learning method called the cross-dataset training convolutional neural network (CDCNN), which uses a general framework for cross-dataset training in age estimation. We adopted convolutional neural networks (CNNs) with VGG-16 architectures pretrained on ImageNet and treated the age estimation problem as a classification problem. For the classification results, softmax is utilized to map the output and provide value refinement. We conducted a series of experiments on the Craniofacial Longitudinal Morphological Face Database (MORPH), Cross-Age Celebrity Dataset (CACD), and Asian Face Age Dataset (AFAD). The results show that simultaneous training on multiple datasets using additional labeled data achieves a more impressive performance when compared to training on a single, independent dataset. Our proposed cross-dataset training model achieves state-of-the-art results on both the AFAD and CACD age estimation benchmarks with great generalizability. |
| first_indexed | 2024-12-22T02:18:36Z |
| format | Article |
| id | doaj.art-2031cea07f93430cb86806ba0b2f466f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T02:18:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2031cea07f93430cb86806ba0b2f466f2022-12-21T18:42:11ZengIEEEIEEE Access2169-35362022-01-0110240482405510.1109/ACCESS.2022.31544039721298Cross-Dataset Learning for Age EstimationBeichen Zhang0https://orcid.org/0000-0001-8093-3887Yue Bao1Department of informatics, Tokyo City University, Setagaya Campus, Tokyo, JapanDepartment of informatics, Tokyo City University, Setagaya Campus, Tokyo, JapanAge estimation from a single human face image has been an important yet challenging task in computer vision and multimedia. Due to the large individual differences in human faces, including the differences in races and genders, the performance of a learning model depends largely on training data. The existing learning methods are challenged by insufficient numbers of images and poor-quality images in datasets, as well as by new low-precision data that are dissimilar to existing training data. In this paper, we propose a learning method called the cross-dataset training convolutional neural network (CDCNN), which uses a general framework for cross-dataset training in age estimation. We adopted convolutional neural networks (CNNs) with VGG-16 architectures pretrained on ImageNet and treated the age estimation problem as a classification problem. For the classification results, softmax is utilized to map the output and provide value refinement. We conducted a series of experiments on the Craniofacial Longitudinal Morphological Face Database (MORPH), Cross-Age Celebrity Dataset (CACD), and Asian Face Age Dataset (AFAD). The results show that simultaneous training on multiple datasets using additional labeled data achieves a more impressive performance when compared to training on a single, independent dataset. Our proposed cross-dataset training model achieves state-of-the-art results on both the AFAD and CACD age estimation benchmarks with great generalizability.https://ieeexplore.ieee.org/document/9721298/Age estimationdeep learningcross-dataset training |
| spellingShingle | Beichen Zhang Yue Bao Cross-Dataset Learning for Age Estimation IEEE Access Age estimation deep learning cross-dataset training |
| title | Cross-Dataset Learning for Age Estimation |
| title_full | Cross-Dataset Learning for Age Estimation |
| title_fullStr | Cross-Dataset Learning for Age Estimation |
| title_full_unstemmed | Cross-Dataset Learning for Age Estimation |
| title_short | Cross-Dataset Learning for Age Estimation |
| title_sort | cross dataset learning for age estimation |
| topic | Age estimation deep learning cross-dataset training |
| url | https://ieeexplore.ieee.org/document/9721298/ |
| work_keys_str_mv | AT beichenzhang crossdatasetlearningforageestimation AT yuebao crossdatasetlearningforageestimation |