Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Facial Expression Recognition from Video Sequences
- All Subjects: Movement (Acting)
- Creators: Turaga, Pavan
Description
In this thesis we consider the problem of facial expression recognition (FER) from video sequences. Our method is based on subspace representations and Grassmann manifold based learning. We use Local Binary Pattern (LBP) at the frame level for representing the facial features. Next we develop a model to represent the video sequence in a lower dimensional expression subspace and also as a linear dynamical system using Autoregressive Moving Average (ARMA) model. As these subspaces lie on Grassmann space, we use Grassmann manifold based learning techniques such as kernel Fisher Discriminant Analysis with Grassmann kernels for classification. We consider six expressions namely, Angry (AN), Disgust (Di), Fear (Fe), Happy (Ha), Sadness (Sa) and Surprise (Su) for classification. We perform experiments on extended Cohn-Kanade (CK+) facial expression database to evaluate the expression recognition performance. Our method demonstrates good expression recognition performance outperforming other state of the art FER algorithms. We achieve an average recognition accuracy of 97.41% using a method based on expression subspace, kernel-FDA and Support Vector Machines (SVM) classifier. By using a simpler classifier, 1-Nearest Neighbor (1-NN) along with kernel-FDA, we achieve a recognition accuracy of 97.09%. We find that to process a group of 19 frames in a video sequence, LBP feature extraction requires majority of computation time (97 %) which is about 1.662 seconds on the Intel Core i3, dual core platform. However when only 3 frames (onset, middle and peak) of a video sequence are used, the computational complexity is reduced by about 83.75 % to 260 milliseconds at the expense of drop in the recognition accuracy to 92.88 %.
ContributorsYellamraju, Anirudh (Author) / Chakrabarti, Chaitali (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis aims to explore the language of different bodies in the field of dance by analyzing
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
ContributorsIyengar, Varsha (Author) / Xin Wei, Sha (Thesis advisor) / Turaga, Pavan (Committee member) / Coleman, Grisha (Committee member) / Arizona State University (Publisher)
Created2016