Feature Guided CNN for Baby’s Facial Expression Recognition

Lin, Qing; He, Ruili; Jiang, Peihe

doi:10.1155/2020/8855885

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Table 3 compares these two networks and demonstrates that the suggested approach performs better. The suggested approach is shallow in comparison to VEFSO-DLSE [18], CNN gets a better average accuracy result of 97.4% and a cross-validation accuracy of 96.21%. The computational advantage of the proposed method achieves through the compact in size with the floating point operations per second of 1.54M.…”

Section: Testingmentioning

confidence: 96%

“…For extracting local temporal and spatial features, a two-stream CNNs model is used. Models based on transfer learning, including VGG16, Resnet 18, and 50, have been suggested for recognizing adult facial emotions [13][14][15]. In order to distinguish the newborn's facial emotions from images, a deep neural network must be built since infant facial expression recognition is necessary in parenting care.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine and Deep Learning Techniques to Facial Emotion Recognition in Infants

Pandyan¹,

Sindha²,

Kannapiran³

et al. 2023

Emotion Recognition - Recent Advances, New Perspectives and Applications

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Infant facial expression recognition is one of the most significant areas of research in the field of computer vision and surveillance parental care. It is essential for both the early diagnosis of medical conditions and intelligent interpersonal interactions. Despite recent improvements in face detection, feature extraction techniques, and expression categorization methods, it is still difficult to develop an automated system employing deep learning methods that achieves the goal of recognizing infant emotions. The prime aim of this chapter is to present a comprehensive framework for recognizing infant emotions using machine learning and deep learning algorithms on the dataset for infant emotions currently accessible. The proposed model directs future research on early detection of infant emotions and has the ability to identify emotional-related medical problems. This article will incorporate the findings on infant emotion recognition required to address the parental supervision and enhance intelligent interpersonal relationships.

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Section: Testingmentioning

confidence: 96%

Section: Deep Learningmentioning

confidence: 99%

Application of Machine and Deep Learning Techniques to Facial Emotion Recognition in Infants

Pandyan¹,

Sindha²,

Kannapiran³

et al. 2023

Emotion Recognition - Recent Advances, New Perspectives and Applications

View full text Add to dashboard Cite

show abstract

“…Qing et al [35] performed facial expression recognition on babies. They introduced a novel dataset, namely, BabyExp.…”

Section: Child Fer Datasetsmentioning

confidence: 99%

Recognition of Children’s Facial Expressions Using Deep Learned Features

Laraib,

Shaukat,

Khan

et al. 2023

Electronics

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The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact that children develop emotional awareness at an early stage. This highlights the need to explore how machines can recognize facial expressions in children, although the absence of a standardized database poses a challenge. In this study, we propose a system that employs Convolutional-Neural-Network (CNN)-based models, such as VGG19, VGG16, and Resnet50, as feature extractors, and Support Vector Machine (SVM) and Decision Tree (DT) for classification, to automatically recognize children’s expressions using a video dataset, namely Children’s Spontaneous Facial Expressions (LIRIS-CSE). Our system is evaluated through various experimental setups, including 80–20% split, K-Fold Cross-Validation (K-Fold CV), and leave one out cross-validation (LOOCV), for both image-based and video-based classification. Remarkably, our research achieves a promising classification accuracy of 99% for image-based classification, utilizing features from all three networks with SVM using 80–20% split and K-Fold CV. For video-based classification, we achieve 94% accuracy using features from VGG19 with SVM using LOOCV. These results surpass the performance of the original work, which reported an average image-based classification accuracy of 75% on their LIRIS-CSE dataset. The favorable outcomes obtained from our research can pave the way for the practical application of our proposed emotion recognition methodology in real-world scenarios.

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