Facial attribute classification by deep mining inter‐attribute correlations
Abstract Face attribute classification (FAC) has received considerable attention due to its excellent application value in bio‐metric verification and face retrieval. Current FAC methods suffer two typical challenges: complex inter‐attribute correlations and imbalanced learning. Aims at the challeng...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cvi2.12171 |
| _version_ | 1797846301982851072 |
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| author | Na Liu Fan Zhang Liang Chang Fuqing Duan |
| author_facet | Na Liu Fan Zhang Liang Chang Fuqing Duan |
| author_sort | Na Liu |
| collection | DOAJ |
| description | Abstract Face attribute classification (FAC) has received considerable attention due to its excellent application value in bio‐metric verification and face retrieval. Current FAC methods suffer two typical challenges: complex inter‐attribute correlations and imbalanced learning. Aims at the challenges, presents an end‐to‐end FAC framework with integrated use of multiple strategies, which consists of a convolutional neural network (CNN) and a graph convolutional network (GCN). The GCN is used to model the semantic correlations among attributes and capture inter‐dependency among them. The correlation information learnt via the GCN is used to guide the learning of the inter‐dependent classification features of the FAC network. An adaptive thresholding strategy and a boosting scheme are adopted to alleviate the effect of the class‐imbalance. To deal with the task imbalance problem, a new dynamic weighting scheme is proposed to update the weight of each attribute classification task in the training process. We apply four evaluation metrics to evaluate the proposed method. Experimental results show all the proposed strategies are effective, and our approach outperforms state‐of‐the‐art FAC methods on two challenging datasets CelebA and LFWA. |
| first_indexed | 2024-04-09T17:52:44Z |
| format | Article |
| id | doaj.art-9624d2af5f904fa8ad825e8dc75030ac |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-04-09T17:52:44Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-9624d2af5f904fa8ad825e8dc75030ac2023-04-15T11:16:51ZengWileyIET Computer Vision1751-96321751-96402023-04-0117335236510.1049/cvi2.12171Facial attribute classification by deep mining inter‐attribute correlationsNa Liu0Fan Zhang1Liang Chang2Fuqing Duan3School of Artificial Intelligence Beijing Normal University Beijing ChinaSchool of Artificial Intelligence Beijing Normal University Beijing ChinaSchool of Artificial Intelligence Beijing Normal University Beijing ChinaSchool of Artificial Intelligence Beijing Normal University Beijing ChinaAbstract Face attribute classification (FAC) has received considerable attention due to its excellent application value in bio‐metric verification and face retrieval. Current FAC methods suffer two typical challenges: complex inter‐attribute correlations and imbalanced learning. Aims at the challenges, presents an end‐to‐end FAC framework with integrated use of multiple strategies, which consists of a convolutional neural network (CNN) and a graph convolutional network (GCN). The GCN is used to model the semantic correlations among attributes and capture inter‐dependency among them. The correlation information learnt via the GCN is used to guide the learning of the inter‐dependent classification features of the FAC network. An adaptive thresholding strategy and a boosting scheme are adopted to alleviate the effect of the class‐imbalance. To deal with the task imbalance problem, a new dynamic weighting scheme is proposed to update the weight of each attribute classification task in the training process. We apply four evaluation metrics to evaluate the proposed method. Experimental results show all the proposed strategies are effective, and our approach outperforms state‐of‐the‐art FAC methods on two challenging datasets CelebA and LFWA.https://doi.org/10.1049/cvi2.12171image classificationimage processingimage recognition |
| spellingShingle | Na Liu Fan Zhang Liang Chang Fuqing Duan Facial attribute classification by deep mining inter‐attribute correlations IET Computer Vision image classification image processing image recognition |
| title | Facial attribute classification by deep mining inter‐attribute correlations |
| title_full | Facial attribute classification by deep mining inter‐attribute correlations |
| title_fullStr | Facial attribute classification by deep mining inter‐attribute correlations |
| title_full_unstemmed | Facial attribute classification by deep mining inter‐attribute correlations |
| title_short | Facial attribute classification by deep mining inter‐attribute correlations |
| title_sort | facial attribute classification by deep mining inter attribute correlations |
| topic | image classification image processing image recognition |
| url | https://doi.org/10.1049/cvi2.12171 |
| work_keys_str_mv | AT naliu facialattributeclassificationbydeepmininginterattributecorrelations AT fanzhang facialattributeclassificationbydeepmininginterattributecorrelations AT liangchang facialattributeclassificationbydeepmininginterattributecorrelations AT fuqingduan facialattributeclassificationbydeepmininginterattributecorrelations |