Recognizing product emotions using deep learning for subtle expression recognition
Date of Publication
2017
Document Type
Master's Thesis
Degree Name
Master of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Merlin Teodosia C. Suarez
Defense Panel Chair
Macario O. Cordel, II
Defense Panel Member
Joel P. Ilao
Rafael A. Cabredo
Merlin Teodosia C. Suarez
Abstract/Summary
A variety of products are designed and manufactured depending on the demands of the consumers. To further develop better products to the increasing wants and needs of the society, insights regarding these products were collected and analyzed. One aspect of these consumer insights is commonly known as product emotion. It is the study of customer's behavior and appreciation when interacting with a product.
One notable system had explored product emotion recognition via facial expression recognition. However, a traditional machine learning approach was used which utilized distances of facial points as features. Throughout his research, it has been observed that product emotions are difficult to recognize. These expressions show low intensity of emotions called, subtle expressions.
Deep learning, on the other hand, had proven to be a powerful approach in image classification because it learns multiple representations of patterns which make it different from using a specific hand-crafted feature: distances of facial points. For this reason, a best performing convolutional neural network was developed which is a product of extensive experiments to recognize subtle expressions from product emotions. Best results came from a 10 layer deep network. The final results are 70.69% top-3 accuracy, 60.08% top-1 accuracy, and 82.46% top-1 accuracy using 7, 3, and 2 emotion labels respectively. Moreover, this research compared the performance of the model with humans. The model performed better than humans which achieved 75% against 62.5%.
Abstract Format
html
Language
English
Format
Electronic
Accession Number
CDTG007054
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
1 computer disc ; 4 3/4 in.
Keywords
Consumers' preferences; Consumer behavior
Recommended Citation
Abergos, R. M. (2017). Recognizing product emotions using deep learning for subtle expression recognition. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/5834