Affect recognition for handheld devices
Date of Publication
Bachelor of Science in Computer Science
College of Computer Studies
Awarded as best thesis, 2013
Jocelynn W. Cu
Defense Panel Chair
Ethel Chua Joy Ong
Defense Panel Member
Joel P. Ilao
Ralph Vincent J. Regalado
This study focuses on the development of an affect recognition system for Android handheld devices following the client-server framework, where data acquisition and display of outputs are done on the handheld device while feature extraction and classification are dine on the Java application server running on a computer. Data acquisition is done through a math game where the subject is recorded while playing the game. Motion history images (MHI) and edge orientation histogram (EOH) were used to represent the face. Categorical labels " Interest, Amusement and Neutral " are the emotions used and these are classified through a decision tree model following the C4.5 or J48 algorithm with an accuracy score of 89% and 94%.
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
1v. various foliations : illustrations (some colored) ; 28 cm.
Go, G. O., Ling, G. C., & Uy, T. T. (2013). Affect recognition for handheld devices. Retrieved from https://animorepository.dlsu.edu.ph/etd_honors/372