Facial expression recognition in Filipino sign language
Date of Publication
Master of Science in Computer Science
College of Computer Studies
Clement Y. Ong
Defense Panel Chair
Conrado D. Ruiz, Jr.
Defense Panel Member
Jocelyn W. Cu
Merlin Teodosia C. Suarez
Filipino Sign Language (FSL) is a mode of communication for the Deaf in the Philippines. Though it has two components, namely manual and non-manual signals, most research works focus on manual signals only. However, non-manual signals play a significant role in Sign Language Recognition (SLR) as it can be mixed freely with manual signals, often changing the meaning of the signs. Internationally, there have been numerous researches regarding non-manual signals. However, most of these focused on the semantic and lexical functions only.
This study focused on recognizing facial expressions in FSL that convey Types of Sentences (i.e. question, statement, and exclamation), Degrees of Adjectives (i.e. absence, presence, and high presence), and Emotions (i.e happy, fear/surprise, sad, and disgust/anger). The data were collected using Microsoft Kinect for Windows 2.0. After the data annotation, co-occurrences of the different categories were discovered. Thus, individual experiments were conducted for each category.
Based from the results of extensive experiments, head rotation angles and Animation Units of different facial features from peak facial expressions are not enough to represent each category of the facial expressions in FSL. Adding classes from other categories as features and reducing features through Genetic Algorithm generally improved the performances significantly. Based from the observations of the raw data, some important features are not represented. Thus, further research on the data representation is necessary to improve the performances.
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
1 disc ; 4 3/4 inches
Sign language; Philippine Sign Language; Facial expression--Philippines
Rivera, J. C. (2017). Facial expression recognition in Filipino sign language. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/5558