Abstract
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.
Highlights
Facial expressions play an important role in human being’s communication. e ability to differentiate genuine displays of emotional experience from the posed ones is very important for dealing with day-to-day social interactions
To address the aforementioned issues, we propose a new image dataset with expression labels of baby faces for automatic facial expression recognition
The performance of these methods on the BabyExp is significantly lower than that on the adult dataset SFEW2.0, 54.45% on SFEW2.0 vs. 39.7% on BabyExp and 58.14% on SFEW2.0 vs. 40.78% on BabyExp, indicating that baby faces are greatly different from the adult faces, and it is important for developing facial expression recognition approaches for baby images
Summary
Facial expressions play an important role in human being’s communication. e ability to differentiate genuine displays of emotional experience from the posed ones is very important for dealing with day-to-day social interactions. Most of the existing works and datasets [7,8,9,10,11] focus on analyzing adult faces, which ignore how to analyze facial expressions from baby facial images. Some datasets include children, there are very few images of very young children None of these datasets is designed to explore the expression of babies. E first reason is that the community has not realized the application values of analyzing baby’s facial expression. There are many applications of analyzing the facial expressions of babies, such as advertising marketing for parents, intelligent family child care, and scientific parenting. E second reason may be traced to the additional challenge of obtaining the baby face datasets with accurate expression labels There are many applications of analyzing the facial expressions of babies, such as advertising marketing for parents, intelligent family child care, and scientific parenting. e second reason may be traced to the additional challenge of obtaining the baby face datasets with accurate expression labels
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