Master of Computer Science (M.C.S.), University of Dayton, 2022, Computer Science

A picture is worth a thousand words. A single image has the power to influence individuals and change their behaviour, whereas a single word does not. Even a barely visible image, displayed on a screen for only a few milliseconds, appears to be capable of changing one's behaviour. In this thesis, we experimentally investigated the relationship between facial expressions and perceived emotions. To this end, we built two datasets, namely, the image dataset for image emotion analysis and the face dataset for expression recognition. During the annotation of the image dataset, both facial expressions and perceived emotions are recorded via a mobile application. We then use a classifier trained on the face dataset to recognize the user's expression and compare it with the perceived emotion.

Committee: Tam Nguyen (Advisor) Subjects: Computer Science

Master of Sciences (Engineering), Case Western Reserve University, 2021, EECS - Computer and Information Sciences

The use of intelligent human/computer interaction systems has become an irreplaceable part of a student's life, be it the extensive use of personal mobility vehicles like smart-scooters, or bicycles or the use of biometric authentication systems for any kind of authentication, the use of intelligent human-computer interaction systems has become an irreplaceable part of a student's life. The aim of this thesis is to propose an IoWT and computer vision-based solution that enhances campus safety focusing on the personal mobility vehicle, Smart-scooters and facial recognition system for students, faculties and, workers wearing masks on campus. The thesis presents a one-of-a-kind "Smart-Scooter rider-assistance system," STEADi, which focuses on the personal mobility vehicles such as smart scooters safety for helping the riders while riding the scooter on side-roads or the sidewalks. The system proposed in this thesis uses a self-training parallel Masked face recognition system for authorization and, sensors for safety monitoring.

Committee: Ming-Chun Huang Dr. (Advisor); Yanfang (Fanny) Ye Dr. (Committee Member); An Wang Dr. (Committee Member); Yinghui Wu Dr. (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science in Engineering, Youngstown State University, 2020, Department of Electrical and Computer Engineering

The Driver's Safety Analyzer was designed after deeply researching over the subject and realizing the need of it in the world. The car safety system, in a whole, is developed to protect the driver from having a car accident. Breaking the safety system in different parts, first, there will be the controlling system that prevents the car from leaving its lane if there is another car in its blind spot. Second, the system which automatically stops the car when approaching a vehicle. Third the system is developed to protect the driver from the car accident, such as the airbags, etc. However, it is known that many car accidents are caused by the driver's irresponsibility. According to psychologists, the human beings are, by average, overconfident of themselves. They will always assume they are above average (more capable than an ordinary human). This behavior is what pushes the person to commit wrong decisions such as, driving after drinking alcoholic beverages, driving without sleeping, texting while driving, and driving for long periods of hours without resting. The Driver's Safety Analyzer is developed to possibly solve the overconfidence behavior problem. Using microprocessor, microcomputer, and coding the system can monitor the driver's behavior and control the environment preventing the driver to commit wrong decisions.

Committee: Coskun Bayrak PhD (Advisor); Frank Li PhD (Advisor); Faramarz Mossayebi PhD (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering; Engineering; Experiments; Systems Design

Master of Sciences, Case Western Reserve University, 0, EMC - Mechanical Engineering

This thesis presents two image processing algorithms for facial emotion recognition (FER). The first method uses two pre-processing filters (pre-filters), i.e., brightness and contrast filter and edge extraction filter, combined with Convolutional Neural Network (CNN) and Support Vector Machine (SVM). By using optimal pre-filter parameters in the pre-processing of the training images, the classification of FER could reach 98.19\% accuracy using CNN with 3,500 epochs for 3,589 face images from the FER2013 datasets. The second approach introduces two geometrical facial features based on action units -- landmark curvatures and vectorized landmarks. This method first detects facial landmarks and extracts action unit (AU) features. The extracted facial segments based on the action units are classified into five groups and input to a SVM. The presented method show how individual parameters, including detected landmarks, AU group selection, and parameters used in the SVM, can be examined and systematically selected for the optimal performance in FER. The results after parameter optimization showed 98.38\% test accuracy with training using 1,479 labeled frames of Cohn-Kanade (CK+) database, and 98.11\% test accuracy with training using 1,710 labeled frames of Multimedia Understanding Group (MUG) database for 6-emotion classification. This technique also shows the real-time processing speed of 6.67 frames per second (fps) for images with a 640x480 resolution. The novelty of the first approach is combining image processing filters with CNN to enhance CNN performance. As for the second approach, it systematically analyzed the effectiveness of proposed geometric features and implemented FER in real-time. The demonstrated algorithms have been applied on human-robot interaction (HRI) application platform - social robot ``Woody" for testing. The presented algorithms have been made publicly available.

Committee: Kiju Lee (Committee Chair); Kathryn Daltorio (Committee Member); Frank Merat (Committee Member) Subjects: Computer Science; Mechanical Engineering; Robotics

Doctor of Psychology (Psy.D.), Xavier University, 2019, Psychology

Non-suicidal self-injury (NSSI) is a growing area of concern in both clinical and non-clinical populations. Understanding the motivations for engaging in this behavior as well as the characteristics of individuals who engage in NSSI are crucial for developing maximally effective interventions. Previous research has indicated that while nearly all self-injurers report doing so as a way of regulating emotions, a slightly smaller proportion (approximately 85%) of individuals who engage in NSSI report doing so for interpersonal reasons – for example, as a way of communicating with others (Turner, Chapman, & Layden, 2012). The current study sought to examine characteristics of individuals who endorse interpersonal functions of self-injury in comparison to self-injurers who do not endorse interpersonal functions of self-injury and to non-self-injuring control participants. It was hypothesized that individuals who endorsed interpersonal NSSI would have greater deficits in social problem-solving and facial emotion recognition compared to self-injurers who do not endorse interpersonal NSSI and to control participants. There were no significant differences between the three groups on facial emotion recognition abilities. A one-way MANOVA indicated that both groups of self-injuring participants had poorer social problem-solving abilities compared to control participants. It may be that individuals with NSSI utilize self-injury as a coping mechanism to the detriment of more effective social problem-solving strategies.

Committee: Nicholas Salsman Ph.D. (Advisor) Subjects: Clinical Psychology; Mental Health; Psychology

Doctor of Philosophy, The Ohio State University, 2017, Psychology

This study examines the differential impact a traditional social skills curriculum, SkillStreaming, and a novel, drama-based social skills intervention, the Hunter Heartbeat Method (HHM), on the core social skills deficits associated with autism spectrum disorder (ASD) and the brain-basis of these deficits. Forty children aged 8-14 years with ASD were recruited to participate in a 12-week social skills intervention. Participants were randomly assigned to receive drama-based or traditional social skills intervention once weekly. Previous research on SkillStreaming and HHM in children with ASD had reported improvement in behavioral measures of social functioning from pre- to post-intervention. Despite evidence of treatment response for both interventions, clear differences between these two types of social skills intervention exist. SkillStreaming is a highly structured, curriculum-based intervention during which specific social skills, such as making a friend or having a conversation are explicitly taught via didactic instruction, modeling, and rehearsal. Conversely, in the HHM, although core elements of modeling and repeated practice with feedback are used, there are no “skills” taught, rather, children learn drama-games that implicitly target core deficits associated with ASD (e.g., eye contact, facial emotion recognition, integration of speech and gesture). Research on drama-based interventions for children with ASD is an emerging literature; previous research has indicated that this treatment is well liked by children and, like traditional treatments, is associated with measurable improvement from pre- to post-intervention. Little is known, however, about how differences in treatment modality (didactic versus experiential) and skills taught (higher-level versus foundational) impact skill acquisition and generalization. It is possible that these key differences impact the neurological substrate of social learning, which may have downstream consequences for skill (open full item for complete abstract)

Committee: Marc Tassé PhD (Advisor); Zhong-Lin Lu PhD (Committee Member); Luc Lecavalier PhD (Committee Member) Subjects: Psychology

Artium Baccalaureus (AB), Ohio University, 2016, Political Science

This thesis attempts to investigate and advance understanding of polarizing leadership through a case study of Israeli Prime Minister Benjamin Netanyahu. The study utilizes operational code analysis as the basis of investigation, examining Netanyahu through his speeches, interviews, and social media. Qualitative and quantitative methods are utilized, including applying George's operational code questions and running a sample of texts through VICS coding. Facial recognition technology is also used to demonstrate new methods of collecting data for the purpose of leadership studies. Though VICS coding results in fairly neutral results for most measures, both facial recognition software and qualitative analysis suggest that Netanyahu may harbor a more negative, conflictual operational code. Qualitative analysis also provides a much greater wealth of nuanced information that helps to understand Netanyahu's belief system and likely actions. In the process, this study provides evidence of information overlooked in VICS coding that should be better addressed moving forward. It also suggests that Netanyahu is best understood as a realist or pragmatic realist who is most concerned with maintaining security through a power imbalance. Conclusions drawn suggest that there is little chance for Israel to obtain peace with its neighboring countries under Netanyahu's leadership and may also provide broader implications and research directions regarding polarizing leadership as a whole.

Committee: Nukhet Sandal Dr. (Advisor) Subjects: Middle Eastern Studies; Personality; Political Science; Psychology

Master of Arts in Psychology, Cleveland State University, 2015, College of Sciences and Health Professions

Intro: Borderline personality disorder (BPD) is a mood disorder that affects 2-4% of the general population, up to 20% of psychological inpatients, and 10% outpatients. It is characterized by unstable affect, behavior, mood, interpersonal relationships, and self-image, and tends to stem from a history of abuse. The DSM-5 scales are labeled as: impulsivity, affect inability, abandonment, unstable relationships, self-image, suicide, emptiness, anger, and quasi-psychotic states. A general finding shows those with BPD tend to have difficulty recognizing and reacting to negative emotions (mainly fear, anger, and disgust). Additionally, researchers have found the brain areas that relate to emotion, planning, attention, memory, and decision-making are smaller in borderlines than healthy subjects. Objective: The purpose of this study was to examine participants with subclinical borderline features and determine the relationships between facial affect recognition deficits. Methods: Two-hundred-and-three potential participants were screened using the Borderline Personality Questionnaire (BPQ). Thirty-five undergraduates from Cleveland State University participated in a computer-based study assessing reaction times (RT) and accuracy to Ekman's Pictures of Facial Affect, the now-standard emotional facial stimuli. Results: The majority of participants were Caucasian (68.8%), female (88.6%), and right-handed (94.3%). Mean age was 20.89 ± 4.75 (range= 23). There were 3 (8.6%) subjects of Hispanic ethnicity. Sixteen (45.7%) of the 35 subjects exhibited high borderline traits, as defined as scoring at least 1.5 standard deviations above the mean on the BPQ. There were no significant differences comparing RT and accuracy between groups (all p values = .124). With regard to lateralization, there is a significant difference in the relative disgust index when comparing borderlines (M= .61 ± .08) to controls (M= .73 ± .12) (t(33)=1.31, p= .002). Conclusions: Our sample of adults with (open full item for complete abstract)

Committee: Amir Poreh PhD (Advisor); Boaz Kahana PhD (Committee Member); Andrew Slifkin PhD (Committee Member) Subjects: Personality Psychology

MA, University of Cincinnati, 2013, Arts and Sciences: Psychology

The goal of this study was to investigate the social cognitive domains of facial affect processing and emotional intelligence in patients who had undergone anterior temporal lobectomy (ATL) for the treatment of medically intractable temporal lobe epilepsy. It was hypothesized that patients who underwent right ATL would perform more poorly than left ATL patients on measures of facial affect processing and emotional intelligence. Additionally, we expected poorer performance on measures of social cognition to predict poorer psychosocial functioning. Participants were sixteen individuals who had undergone ATL at the University of Cincinnati Medical Center. They completed a facial affect processing battery, a performance based emotional intelligence test, neuropsychological measures (memory, attention executive ability, and confrontation naming), and self-report questionnaires of quality of life and psychosocial functioning. Data from 16 participants (8 right ATL; 8 left ATL) were analyzed. Participants with right ATL were less accurate than participants with left ATL in their ability to identify the presence and rate the intensity of emotions in facial expressions. The right ATL group performed more slowly than the left while comparing the relative intensity of emotions depicted in two faces and when rating the intensity of the emotional valance of facial expressions (p < .10 for all comparisons). Despite their slower performance, the right ATL group was significantly more accurate than the left ATL group in their ability to compare the relative intensity of emotions depicted in two faces (p < .10). Poorer ability to rate the relative intensity of emotions depicted in faces and to incorporate one's own emotions into decision making were significantly related to poorer self-reported functioning on multiple domains of quality of life and psychosocial functioning, all p < .05.

Committee: Paula Shear Ph.D. (Committee Chair); Steven Howe Ph.D. (Committee Member); Gerald Matthews Ph.D. (Committee Member); Michael Privitera M.D. (Committee Member) Subjects: Clinical Psychology

MA, University of Cincinnati, 2001, Arts and Sciences : Psychology

Patients diagnosed with bipolar disorder (BPD), by definition, have problems with emotional regulation. However, it remains uncertain whether these patients are also deficient at processing other people's emotions, particularly while in the manic state. The present study examined the ability of 25 manic patients and 25 healthy participants on tasks of facial recognition and facial affect recognition at three different presentation durations: 500ms, 750ms, and 1000ms. The groups did not differ in terms of age, education, sex, race or estimated IQ. In terms of facial recognition, the groups did not differ significantly on either a novel computerized facial recognition task or the Benton Facial Recognition task. In contrast, the BPD group performed significantly more poorly than did the comparison group on a novel facial affect discrimination task and a novel facial affect labeling task at 500ms presentation duration. Facial affect processing was not impaired at longer presentation durations. Further, the patient group slower on all three computerized tasks. This study indicates that patients with BPD may need more time to examine facial affect, but exhibit a normal ability to recognize faces.

Committee: Stephen Strakowski (Advisor) Subjects: Psychology, General

MA, University of Cincinnati, 2008, Arts and Sciences : Psychology

This study sought to replicate the finding that adolescents with bipolar disorder (BPD) have facial affect processing deficits as well as to examine the relationship between these labeling deficits and social choices based on affective information. Participants with bipolar disorder were compared with healthy adolescents on tasks of facial affect recognition, facial recognition, attention, and facial affect interpretation. These results suggest that adolescents with BPD have mild reductions in their ability to label emotions relative to healthy adolescents. Additionally, response speed was shown to be quicker for those with bipolar disorder when making social judgments about happy, angry and neutral faces. No significant differences were found between groups when making social judgments with respect to accuracy. Finally, groups did not differ with respect to response speed when labeling or making forced choice judgments about affect.

Committee: Paula Shear PhD (Committee Chair); Robert Stutz PhD (Committee Member); Melissa DelBello PhD (Committee Member) Subjects: Behaviorial Sciences; Developmental Psychology; Mental Health; Psychological Tests; Psychology

Master of Science (MS), Ohio University, 2010, Computer Science (Engineering and Technology)

This thesis presents a novel approach for recognizing facial expressions by incorporating class-mean Gabor responses of sampled images of human facial expressions and kernel principal component analysis (kernel PCA) with fractional polynomial power models. A mean vector of features is obtained with Gabor filters from a class of images instead of the more common method in which features are obtained from individual images. The computational cost of spatial convolutions on mean features of a class is less than the same type of convolutions with individual features. The dimensionality of mean features from Gabor filters is further reduced by using a kernel PCA technique with polynomial kernels. The kernel PCA technique is extended to use fractional power polynomial models for facial expression recognition. The proposed approach has the advantage of doing fewer projections than other facial expression recognition approaches that use traditional kernel PCA models. The proposed approach of class-mean Gabor responses has higher accuracy than existing systems that use the kernel PCA technique with class-mean image responses only.

Committee: David M. Chelberg PhD (Advisor); Jun Dong Liu PhD (Committee Member); Frank Drews PhD (Committee Member) Subjects: Computer Science; Electrical Engineering; Engineering

Doctor of Philosophy (PhD), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

Video events contain rich semantic information. Using computational approaches to analyze video events is very important for many applications due to the desire to interpret digital data in a way that is consistent with human knowledge. This thesis investigates object-based video event analysis based on a statistical framework. Within the proposed architecture for object-based video event understanding, object detection is addressed by model-based approaches with the integration of prior color/shape knowledge and recognition feedback. Object classification is investigated as shape-based image retrieval. The relevance feedback is used to adaptively derive basis vectors to capture a user's perceptual preferences. The major focus of this thesis is concerned with statistical modeling of facial event recognition. Two hidden Markov model (HMM) based approaches are presented. The first approach tracks the deformation of facial components in image sequences via active shape models (ASMs) and extracts geometric-based features for facial gestures. The interaction between upper and lower facial components is explicitly modeled via coupled HMMs (CHMMs) by introducing coupled dependencies between hidden variables. The second approach automatically locates face regions in each image frame via eigenanalysis, extracts multi-band appearance features based on Gabor filtering, and models the spatio-temporal stochastic structure of facial image sequences using hierarchical HMMs (HHMMs). The major contributions of this thesis include: 1) a fully automatic person-independent facial expression recognition prototype system; 2) modeling of the spatio-temporal structure of facial image sequences within a hierarchical framework; 3) derived generalized inference and learning algorithms of HHMMs for observation sequences with known multi-scale structures; 4) improved performance of ASMs for facial component tracking by using dynamic programming based search with contextual constraints; 5) expli (open full item for complete abstract)

Committee: David Chelberg (Advisor) Subjects:

PHD, Kent State University, 2011, College of Arts and Sciences / Department of Psychological Sciences

Facial emotion recognition has a central role in human communication. Generalized anxiety disorder (GAD) and major depressive disorder (MDD) have been associated with deficits in social and interpersonal functioning raising the question as to whether these conditions are also associated with deficits in facial emotion recognition. In addition to being associated with interpersonal difficulties, GAD and MDD overlap substantially at the genotypic and phenotypic level. However, these mental health conditions differ at the cognitive level in that GAD is associated with thoughts revolving around threatening information, whereas thoughts in depression are related to loss, failure, and sadness. These unique cognitive mechanisms may also play a role in the process of facial emotion recognition resulting in differential patterns of responses to facial expressions of emotions for GAD and depression. Although facial emotion recognition has been investigated in MDD, no studies to date have examined this process in GAD. The goals of the present study were threefold: 1) Examine the overall accuracy of facial emotion recognition as well as that for specific emotions in GAD, MDD, and comorbid MDD+GAD, 2) Examine misattributions in facial expression recognition in response to anger, sadness, and neutral expressions in GAD, MDD, and comorbid MDD+GAD, and 3) Investigate the relationship of facial emotion recognition and interpersonal functioning in the context of GAD and MDD. A sample of 90 participants with GAD, MDD, comorbid MDD+GAD, and healthy controls completed a facial emotion recognition task and a battery of self-report measures. The findings did not support a general or specific deficit in facial emotion recognition in MDD and GAD. Further, individuals with MDD and GAD did not differ in their responses to neutral facial expressions nor to other basic emotions. The findings are discussed in the context of future clinical and research directions.

Committee: David Fresco PhD (Advisor); John Gunstad PhD (Committee Member); John Updegraff PhD (Committee Member); David Hussey PhD (Committee Member); William Kalkhoff PhD (Committee Member) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2008, Computer Engineering

In this thesis, we propose a cognitive information processing system (cognitive processing) on an evolutionary platform for retargetable applications such as facial image recognition, image feature extraction, evolvable filters, and environmental information tracking. Cognitive processing can process multiple-sensory information on an automated system such as an unmanned vehicle or a surveillance unit in a remote site to avoid harsh terrain. Evolutionary platform supports the ability to change information processing behavior to comply with ever changing environment in order to accomplish a mission objective. The cognitive processing model can overcome particular difficulties to traditional search, exploration, and engineering decision making applications. The proposed cognitive strategies emphasize the decomposition of multi-sensory information, the re-construction of internal representations, and the cognitive processing of combined information which yield sub-optimal solutions and indicate best local system direction. Several applications, such as facial image recognition and digital signal processing, are used to verify our models and compare them with other well-known approaches. The derived simulation and synthesized results show that the proposed cognitive processing model on evolutionary platform attains better performance than those of the conventional methods.

Committee: Chris Papachristou PhD (Committee Chair); Daniel Saab PhD (Committee Member); Frank Merat PhD (Committee Member); Vira Chankong PhD (Committee Member); Frank Wolff PhD (Committee Member) Subjects: Computer Science; Engineering

Ph.D., Antioch University, 2013, Leadership and Change

Bringing innovation to the marketplace for new products and services involves creativity, a culture in which change flourishes, and leadership that thrives on transformation and complexity. This study explored the potential for market disruption or change based on innovations involving patents granted to nonfinancial services organizations that could affect financial services, specifically consumer or retail bank products. It involved analyzing documents related to recently granted patents and completing a mixed methods survey integrating the Delphi research technique. This method required multiple iterations of a survey presented to expert panelists or industry thought leaders to attempt to gain consensus ("Consensus", 2011) or general agreement by the group (Tersine & Riggs, 1976). With this research method, the goal is to gain an understanding of initial individual perspectives. Through an iterative process, then determine if, as a group, they can move toward a common vision of what is likely to happen after viewing other's perspectives. This research was specific to two innovations for which patents have been granted: facial recognition and digital wallets. Patents can provide insights into potential new developments planned by organizations. In some cases, patents can provide insights into innovation, potential threats, opportunities, or disruptions that could change the way a market operates. The goal of this research was to select two recent patents from many that have been granted, develop theoretical insights, and, through a mixed methods survey integrating the Delphi methodology, identify when or if these patents could have an impact on financial services. This research brought together thought leaders in an anonymous, collaborative approach to assess considerations and provide their perspective on these changes. This study served to help leaders drive innovation in financial services organizations and to understand how others perceive these inn (open full item for complete abstract)

Committee: Mitchell Kusy Ph.D. (Committee Chair); Jon Wergin Ph.D. (Committee Member); Byrd Jacqueline Ph.D. (Committee Member); Sahm Patricia Ph.D. (Other) Subjects: Banking; Business Administration; Business Community; Entrepreneurship; Information Technology; Intellectual Property; Management; Marketing; Organization Theory; Organizational Behavior; Patent Law; Spirituality; Systems Design; Technology