| Author | Hussain, Akhtar |
| Call Number | AIT Diss. no.CS-12-01 |
| Subject(s) | Intelligent tutoring systems
Bayesian statistical decision theory
|
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Dissertation ; no. CS-12-01 |
| Abstract | Human beings have the ability to detect and understand affective/mental states, and other
social signals when interacting with each other. This ability or intelligence is an important
aspect of their social life in social relationships. Researchers from multi-disciplinary areas
have been trying to incorporate this ability or intelligence in computers through body lan-
guage to make them affective companions of the users for variety of applications. Gestures
either intentional or unintentional are very useful to find the underlying mental or affective
state of a person in any social interaction. Researchers developing interactive and intelligent
computer interfaces are very much interested in extracting meaningful information from
variety of gestures, e.g., self-manipulators that include unintended hand-touch-head (face)
movements. Predicting human behavior from physical activity is an active area of research
for many Affective Computing applications. However, correctly detecting and classifying
human body movements specially unintentional body gestures are a research problem. The
main problem is occlusion in unintended hand-touch-head (face) gesture’s classification due
to similar skin color and texture because when the hand enters in the face region. it merged
with the face so difficult to separate the hand from the face region. However, we propose
a solution for separating hand(s) from face in varying lighting conditions by generating lo-
cal binary patterns using force field features in conjunction with Sobcl edge operator called
(Sobel-LBP).
In this dissertation we performed two experiments one with single context and second with
multi-context scenarios using real and synthetic data. In our first experiment we used vision
based techniques and Bayesian network model for student mental state prediction from un-
intentional hand-touch-head (face) movements in classroom context. After successful clas-
sification of the gestures in the form ofbinary codes using vision based techniques, we code
these different gestures of more than 100 human subjects. and feed these codes manually
in three-layered Bayesian network (BN) to infer the probable mental state with particular
gesture. The first layer shows the mental states to gestures relationships and the second layer
combine the gestures, and SLBP generated binary codes. The proposed scheme when eval-
uated on a our data set in single context scenario collected in real classroom situation and
found promising results with an accuracy of about 85%. The framework will be utilized for
developing intelligent tutoring system.
In our second experiment we used same techniques for predicting the student mental state
in classroom context with multi-context scenarios using real and synthetic data and obtained
75% average accuracy of the predicted result. This result can be improved by increasing the
collection of data sets in different contexts in real time situations for training and testing.
The results show that our proposed system can also be used for various other applications of
Affective Computing.
Our proposed system exhibits that in single and as well as in multi-contexts situations. un-
intentional body gestures can carry useful information about the mental or affective state of
the students that can be used to increase the efficiency of various applications such as an in-
telligent tutoring system by applying an integrated framework of computer vision techniques
and Bayesian network model. |
| Year | 2012 |
| Corresponding Series Added Entry | Asian Institute of Technology. Dissertation ; no. CS-12-01 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Afzulpurkar, Nitin V.; |
| Examination Committee(s) | Guha, Sumanta;Manukid Pamichkun;Abbasi, Abdul Rehman; |
| Scholarship Donor(s) | Higher Education Commission (HEC), Pakiatan;Asian Institute of Technology Fellowship; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2012 |
