Description/Abstract/Artist Statement
Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the base and middle layers are preserved to exploit the existing neural architecture. The investigated method begins with a base-packaged architecture trained on ImageNet. This foundation is then task changed from general object classification to facial expression classification in the first transfer learning step. The second transfer learning step performs a domain change from adult to child data. Finally, the trained network is evaluated on the child facial expression classification task.
Faculty Advisor/Mentor
Khan Iftekharuddin
College Affiliation
College of Engineering & Technology (Batten)
Presentation Type
Poster
Disciplines
Artificial Intelligence and Robotics | Bioimaging and Biomedical Optics | Biomedical Engineering and Bioengineering
Session Title
Poster Session
Location
Learning Commons @ Perry Library
Start Date
3-19-2022 9:00 AM
End Date
3-19-2022 11:00 AM
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Two-Stage Transfer Learning for Facial Expression Classification in Children
Learning Commons @ Perry Library
Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the base and middle layers are preserved to exploit the existing neural architecture. The investigated method begins with a base-packaged architecture trained on ImageNet. This foundation is then task changed from general object classification to facial expression classification in the first transfer learning step. The second transfer learning step performs a domain change from adult to child data. Finally, the trained network is evaluated on the child facial expression classification task.