Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm
Abstract Aiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-06-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12084 |
| _version_ | 1797641508531208192 |
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| author | Di Lu Dapeng Wang Kaiyu Zhang Xiangyuan Zeng |
| author_facet | Di Lu Dapeng Wang Kaiyu Zhang Xiangyuan Zeng |
| author_sort | Di Lu |
| collection | DOAJ |
| description | Abstract Aiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals. |
| first_indexed | 2024-03-11T13:46:40Z |
| format | Article |
| id | doaj.art-c13e39cfbe2c4f6ba2e11368f2d6c661 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-03-11T13:46:40Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-c13e39cfbe2c4f6ba2e11368f2d6c6612023-11-02T10:21:18ZengWileyCAAI Transactions on Intelligence Technology2468-23222023-06-018251853110.1049/cit2.12084Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithmDi Lu0Dapeng Wang1Kaiyu Zhang2Xiangyuan Zeng3School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin ChinaSchool of Measurement and Communication Engineering Harbin University of Science and Technology Harbin ChinaSchool of Measurement and Communication Engineering Harbin University of Science and Technology Harbin ChinaCheriton School of Computer Science University of Waterloo Waterloo Ontario CanadaAbstract Aiming at the problem of long time‐consuming and low accuracy of existing age estimation approaches, a new age estimation method using Gabor feature fusion, and an improved atomic search algorithm for feature selection is proposed. Firstly, texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform. The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales. Secondly, a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing (CIASO‐SA) is presented, which is based on an improved atomic search algorithm and the simulated annealing algorithm. Besides, the CIASO‐SA algorithm introduces a chaos mechanism during atomic initialisation, significantly improving the convergence speed and accuracy of the algorithm. Finally, a support vector machine (SVM) is used to get classification results of the age group. To verify the performance of the proposed algorithm, face images with three resolutions in the Adience dataset are tested. Using the Gabor real part fusion feature at 48 × 48 resolution, the average accuracy and 1‐off accuracy of age classification exhibit a maximum of 60.4% and 85.9%, respectively. Obtained results prove the superiority of the proposed algorithm over the state‐of‐the‐art methods, which is of great referential value for application to the mobile terminals.https://doi.org/10.1049/cit2.12084age estimationatom search algorithmfeature selectionGabor featuresimulated annealing |
| spellingShingle | Di Lu Dapeng Wang Kaiyu Zhang Xiangyuan Zeng Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm CAAI Transactions on Intelligence Technology age estimation atom search algorithm feature selection Gabor feature simulated annealing |
| title | Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm |
| title_full | Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm |
| title_fullStr | Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm |
| title_full_unstemmed | Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm |
| title_short | Age estimation from facial images based on Gabor feature fusion and the CIASO‐SA algorithm |
| title_sort | age estimation from facial images based on gabor feature fusion and the ciaso sa algorithm |
| topic | age estimation atom search algorithm feature selection Gabor feature simulated annealing |
| url | https://doi.org/10.1049/cit2.12084 |
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