Feature Guided CNN for Baby’s Facial Expression Recognition
State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8855885 |
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| author | Qing Lin Ruili He Peihe Jiang |
| author_facet | Qing Lin Ruili He Peihe Jiang |
| author_sort | Qing Lin |
| collection | DOAJ |
| description | State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp. |
| format | Article |
| id | doaj-art-7a899d3a29c34062bc7b865c0aef029a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7a899d3a29c34062bc7b865c0aef029a2025-02-03T06:05:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88558858855885Feature Guided CNN for Baby’s Facial Expression RecognitionQing Lin0Ruili He1Peihe Jiang2Integrated Information Center of Yantai, Yantai 264003, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, ChinaSchool of Opto-Electronic Information Science and Technology, Yantai University, Yantai 264005, ChinaState-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.http://dx.doi.org/10.1155/2020/8855885 |
| spellingShingle | Qing Lin Ruili He Peihe Jiang Feature Guided CNN for Baby’s Facial Expression Recognition Complexity |
| title | Feature Guided CNN for Baby’s Facial Expression Recognition |
| title_full | Feature Guided CNN for Baby’s Facial Expression Recognition |
| title_fullStr | Feature Guided CNN for Baby’s Facial Expression Recognition |
| title_full_unstemmed | Feature Guided CNN for Baby’s Facial Expression Recognition |
| title_short | Feature Guided CNN for Baby’s Facial Expression Recognition |
| title_sort | feature guided cnn for baby s facial expression recognition |
| url | http://dx.doi.org/10.1155/2020/8855885 |
| work_keys_str_mv | AT qinglin featureguidedcnnforbabysfacialexpressionrecognition AT ruilihe featureguidedcnnforbabysfacialexpressionrecognition AT peihejiang featureguidedcnnforbabysfacialexpressionrecognition |