Feature Guided CNN for Baby’s Facial Expression Recognition
Joint Authors
Lin, Qing
He, Ruili
Jiang, Peihe
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lin, Qing& He, Ruili& Jiang, Peihe. 2020. Feature Guided CNN for Baby’s Facial Expression Recognition. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144932
Modern Language Association (MLA)
Lin, Qing…[et al.]. Feature Guided CNN for Baby’s Facial Expression Recognition. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1144932
American Medical Association (AMA)
Lin, Qing& He, Ruili& Jiang, Peihe. Feature Guided CNN for Baby’s Facial Expression Recognition. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144932
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144932