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- All Subjects: EEG
- All Subjects: object detection
Description
Lie detection is used prominently in contemporary society for many purposes such as for pre-employment screenings, granting security clearances, and determining if criminals or potential subjects may or may not be lying, but by no means is not limited to that scope. However, lie detection has been criticized for being subjective, unreliable, inaccurate, and susceptible to deliberate manipulation. Furthermore, critics also believe that the administrator of the test also influences the outcome as well. As a result, the polygraph machine, the contemporary device used for lie detection, has come under scrutiny when used as evidence in the courts. The purpose of this study is to use three entirely different tools and concepts to determine whether eye tracking systems, electroencephalogram (EEG), and Facial Expression Emotion Analysis (FACET) are reliable tools for lie detection. This study found that certain constructs such as where the left eye is looking at in regard to its usual position and engagement levels in eye tracking and EEG respectively could distinguish between truths and lies. However, the FACET proved the most reliable tool out of the three by providing not just one distinguishing variable but seven, all related to emotions derived from movements in the facial muscles during the present study. The emotions associated with the FACET that were documented to possess the ability to distinguish between truthful and lying responses were joy, anger, fear, confusion, and frustration. In addition, an overall measure of the subject's neutral and positive emotional expression were found to be distinctive factors. The implications of this study and future directions are discussed.
ContributorsSeto, Raymond Hua (Author) / Atkinson, Robert (Thesis director) / Runger, George (Committee member) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The goal of the ANLGE Lab's AR assembly project is to create/save assemblies as well as to replicate assemblies later with real-time AR feedback. In this iteration of the project, the SURF algorithm was used to provide object detection for 5 featureful objects (a Lego girl piece, a Lego guy piece, a blue Lego car piece, a window piece, and a fence piece). Functionality was added to determine the location of these 5 featureful objects within a frame as well by using the SURF keypoints associated with detection. Finally, the feedback mechanism by which the system detects connections between objects was improved to consider the size of the blocks in determining connections rather than using static values. Additional user features such as adding a new object and using voice commands were also implemented to make the system more user friendly.
ContributorsSelvam, Nikil Panneer (Author) / Atkinson, Robert (Thesis director) / Runger, George (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05