Gaze-based Classification of Autism Spectrum Disorder

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Abstract

People with autism spectrum disorder (ASD) display impairments in social interaction and communication skills, as well as restricted interests and repetitive behaviors, which greatly affect daily life functioning. Current identification of ASD involves a lengthy process that requires an experienced clinician to assess multiple domains of functioning. Considering this, we propose a method for classifying multiple levels of risk of ASD using eye gaze and demographic feature descriptors such as a subject’s age and gennder. We construct feature descriptors that incorporate the subject’s age and gender, as well as features based on eye gaze patterns. We also present an analysis of eye gaze patterns validating the use of the selected hand-crafted features. We assess the efficacy of our descriptors to classify ASD on a National Database for Autism Research dataset, using multiple classifiers including a random forest, C4.5 decision tree, PART, and a deep feedforward neural network.

Latent Space Representation of Emotion

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Abstract

Facial expression Recognition is a growing and important field that has applications in fields such as medicine, security, education, and entertainment. While there have been encouraging approaches that have shown accurate results on a wide variety of datasets, in many cases it is still a difficult problem to explain the results. Considering this, in this paper, we propose to model flow-based latent representations of facial expressions, which allows us to further analyze the features and grants us more granular control over which features are produced for recognition. Our work is focused on posed facial expressions with a tractable density of the latent space. We investigate the behaviour of these tractable latent space features in the case of subject dependent and independent expression recognition. We employ a flow-based generative approach with minimal supervision introduced during training and observe that traditional metrics give encouraging results. When subject independent expressions are evaluated, a shift towards a stochastic nature, in the probability space, is observed. We evaluate our flow-based representation on the BU-EEG dataset showing our approach provides good separation of classes, resulting in more explainable results.

Quantified Facial Affect Analysis

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Abstract

The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness Dynamics (TED) to quantify the expressiveness of human faces. The proposed algorithm leverages multimodal facial features by incorporating static and dynamic information to enable accurate measurements of facial expressiveness. We show that TED can be used for high-level tasks such as summarization of unstructured visual data, and expectation from and interpretation of automated affect recognition models. To evaluate the positive impact of using TED, a case study was conducted on spontaneous pain using the UNBC-McMaster spontaneous shoulder pain dataset. Experimental results show the efficacy of using TED for quantified affect analysis.

3D Subject Identification Across Large Expression Variation

, University of South Florida

Subject identifcation using 3D facial landmarks.

Gesture Recognition

, University of South Florida

Gesture Recognition for sign language recognition.

Eficient Join Algorithms For Large Database Tables in a Multi-GPU Environment Permalink

News, University of South Florida

We designed three join algorithms with diferent out-of-core data transfer patterns and the same in-core join processing. By a series of experiments, we demonstrated that the inter-GPU communication and data exchange drastically afect the running time of the join algorithms.

Counting frequent patterns in large labeled graphs: a hypergraph-based approach Permalink

News, University of South Florida

We propose a new framework for studying support measures in frequent subgraph mining. This framework transforms pattern and data graph into hypergraphs containing occurrences and instances of the pattern as well as information of the original graph, in contrast to existing overlap graph techniques that only contain the former.

Selected Projects

Research,

A sampling of projects divided into two self-explanatory groups.

Publications

Research,

A list of many publications

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

Hao Li

Ph.D Student, University of South Florida

Zhila Nouri-Lewis

Ph.D Student, University of South Florida

Ran Rui

Ph.D Student, University of South Florida

Ryan Wheeler

M.S. Student, University of South Florida

COP 4710 - Database Design

Undergraduate course, University of South Florida, Spring, 2022

This course covers the fundamentals and applications of database management systems, including data models, relational database design, query languages, and web-based database applications.