Research Team

Principal Investigator

Alice O’Toole, PhD

Alice O'Toole

Prof. Alice O’Toole is a professor in the School of Behavioral and Brain Sciences at The University of Texas at Dallas and currently holds the Aage and Margareta Møller Endowed Chair.

Her research interests include human perception, memory, and cognition, with an emphasis on computational approaches to modeling human information processing. Most recently, her work is focused on the problem of recognizing faces and people. She has approached this problem using methods from psychology, cognitive neuroscience, and computational modeling. Current projects in her lab include comparisons between human and machine-based face recognition, the analysis of face recognition algorithms, person recognition from face, body, and biological motion, and modeling the relation between language and human body shapes. Read more

View Dr. O’Toole’s Curriculum Vitae

Graduate Students

MATTHEW Q. HILL, BS (DOCTORAL STUDENT)

Matthew Hill

Matt is a PhD student interested in the relationship between human cognition and machine learning. He currently works as part of a team evaluating deep neural networks trained for face identification in order to better understand how they encode information about identity, as well as variables like viewpoint and illumination. His previous work investigated the relationship between human body shapes and the language used to describe them by systematically linking 3D body scans with verbal body descriptions.

Publications:

Hill, M. Q., Parde, C. J., Castillo, C. D., Colon, Y. I., Ranjan, R., Chen, J. C., Blanz, V., & O’Toole, A. J. (2019). Deep convolutional neural networks in the face of caricature. Nature Machine Intelligence, 1(11), 522-529.

O’Toole, A. J., Castillo, C. D., Parde, C. J., Hill, M. Q., & Chellappa, R. (2018). Face space representations in deep convolutional neural networks. Trends in cognitive sciences, 22(9), 794-809.

Parde, C. J., Castillo, C., Hill, M. Q., Colon, Y. I., Sankaranarayanan, S., Chen, J. C., & O’Toole, A. J. (2017). Face and image representation in deep cnn features. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (pp. 673-680). IEEE.

Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., & O’Toole, A. J. (2016). Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces. Psychological Science, 27(11), 1486-1497.

ASAL BARAGCHIZADEH, MS, MPA (DOCTORAL STUDENT)

Asal Baragchizadeh

Asal is a third-year PhD student in the cognition and neuroscience program. She received an MS in applied cognition and neuroscience from The University of Texas at Dallas, MPA from Northeastern University, and a double-major BS in biomedical and electrical engineering from Tehran Science and Research University in Iran. Currently, her primary interest is in-person identification from biological motion and its neural correlates. More specifically, using fMRI-adaptation she is studying the discriminability of neural activity patterns elicited in response to identity and actions in selected ROIs and whole brain. Previously, she worked on a project to evaluate Automated Identity Masking (AIM) algorithms in Naturalistic Driving Study (NDS) videos. As a side research project, she has helped a team of researchers at the US Army Graduate Program in Anesthesia Nursing, JBSA-FSH, San Antonio TX to study the effects of tibial and humerus intraosseous administration of epinephrine in a cardiac arrest swine model.

Publications:

Asal Baragchizadeh, Thomas Karnowski, David Blome, & Alice O’Toole (2017). Evaluation of the Automated Identity Masking Method (AIM) in Naturalistic Driving Study (NDS). Twelfth IEEE International Conference on Automatic Face and Gesture Recognition.

Beaumont, D., Baragchizadeh, A., Johnson, C., & Johnson, D. (2017). Effects of tibial and humerus intraosseous administration of epinephrine in a cardiac arrest swine model. American Journal of Disaster Medicine, 11(4), 243-251.

YING “NINA” HU (DOCTORAL STUDENT)

Ying Hu

Ying is a PhD student who is interested in visual perception and cognition. She is currently working on exploring the social trait inferences from faces and bodies. She also works in the team designing a test to measure the skills of forensic face identification examiners on challenging tasks.

Publications:

Hu, Y., Jackson, K., Yates, A., White, D., Phillips, P. J., & O’Toole, A. J. (2017). Person recognition: Qualitative differences in how forensic face examiners and untrained people rely on the face versus the body for identification. Visual Cognition, 1-15.

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., & O’Toole, A. J. (2018). First Impressions of Personality Traits From Body Shapes. Psychological science, 29(12), 1969-1983.

Phillips, P. J., Yates, A., Hu, Y., Hahn, C. A., Noyes, E., Jackson, K., Cavazos, J.G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., Chen, J., Castillo, C. D., Chellappa, R., White, D., and O’Toole, A. J. (2018). Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proceedings of the National Academy of Sciences, 201721355.

Parde, C.J., Hu,Y., Castillo, C., Sankaranarayanan,S., & O’Toole, A.J. (2019). Social trait information in deep convolutional neural networks trained for face identification. Cognitive Science, 43(6), e12729.

GÉRALDINE JECKELN (DOCTORAL STUDENT)

Géraldine Jeckeln

Géraldine joined the cognition and neuroscience doctoral program in 2019. She obtained an MS in applied cognition and neuroscience from The University of Texas at Dallas (2018) and a BA in honors psychology from Concordia University (2015). Presently, her research focuses on the relationship between confidence judgments and face-identification performance. She is also involved in a project designing assessment tools for forensic face identification. Other research interests include wisdom-of-crowds effects and models of collaborative decision-making.

Publications:

Phillips, P.J., Yates, A.N., Hu, Y., Hahn, C.A., Noyes, E., Jackson, K., Cavazos, J.G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., Chen, J-C., Castillo, C.D., Chellappa, R., White, D., & O’Toole, A.J. (2018). Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1721355115.

Jeckeln, G., Hahn, C.A., Noyes, E., Cavazos, J.G., & O’Toole, A. J. (2018). Wisdom of the social versus non-social crowd for face identification. British Journal of Psychology. doi:10.1111/bjop.12291.

Cavazos, J.G., Jeckeln, G., Hu, Y., & O’Toole, A. J. (In Press). Strategies of face recognition by humans and machines. Deep Learning-based Face Analytics. Cambridge University Press.

CONNOR PARDE, BS (DOCTORAL STUDENT)

Connor Parde

Connor is a PhD student in the cognition and neuroscience program. He is interested in face perception and computational modeling of human cognition. His current research focuses on analyzing the performance of face-identification algorithms across image variation, with emphasis on the nature of the face representation stored by the network. More generally, he is interested in category boundaries in learning networks and how different training approaches may effect these boundaries.

Publications:

Hill, M. Q., Parde, C. J., Castillo, C. D., Colon, Y. I., Ranjan, R., Chen, J. C., … & O’Toole, A. J. (2019). Deep convolutional neural networks in the face of caricature. Nature Machine Intelligence, 1(11), 522-529.

Parde, C. J., Hu, Y., Castillo, C., Sankaranarayanan, S., & O’Toole, A. J. (2019). Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification. Cognitive science, 43(6), e12729.

Hu, Y., Parde, C. J., Hill, M. Q., Mahmood, N., & O’Toole, A. J. (2018). First impressions of personality traits from body shapes. Psychological Science, 29(12), 1969-1983

O’Toole, A. J., Castillo, C. D., Parde, C. J., Hill, M. Q., & Chellappa, R. (2018). Face space representations in deep convolutional neural networks. Trends in cognitive sciences, 22(9), 794-809.

Parde, C. J., Castillo, C., Hill, M. Q., Colon, Y. I., Sankaranarayanan, S., Chen, J. C., & O’Toole, A. J. (2017, May). Face and image representation in deep cnn features. In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (pp. 673-680). IEEE.

Lab Affiliated Graduate Student

JAMES RYLAND (DOCTORAL STUDENT)

James Ryland

James is a PhD student studying with Dr. Richard Golden and Dr. O’Toole, who carries out research on visual cognition and neuroscience. He designs and implements neural network models for object recognition that are consistent with cognitive theories of how the brain performs visual recognition. In addition, he tests these models to see if their behavior and representations are consistent with human behavior and neural organization. Although now focused on visual recognition, James has many other interests such as self-organization, spatial awareness, motor planning, and visualization.

Undergraduate Students

VICTORIA HUANG

Victoria Huang

Victoria is an undergraduate cognitive science major at The University of Texas at Dallas. Her interests lie primarily in human recognition of faces and bodies. She is currently working on a study investigating the relationships among human perceptions of body shapes, body descriptions, and personality trait judgments. In the future, Victoria plans on pursuing a career in medicine.

PARISA JESUDASEN

Parisa Jesudasen

Parisa is a sophomore at The University of Texas at Dallas studying psychology. She is an undergraduate research assistant working on two experiments 1) evaluating Automated Identity Masking (AIM) algorithms with human perception and Deep Convolutional Networks (D-CNNs) and 2) person identification from biological motion. Her interests include person recognition and its application in the medical field, specifically genetic disease recognition in relation to face and body perception. After graduating, she will pursue a degree in medicine.

SNIPTA MALLCIK

Snipta Mallcik

Snipta is a junior at The University of Texas at Dallas double majoring in computer and cognitive science. Her research initiatives are centered on the analysis of facial recognition algorithms with a focus on using Deep Convolutional Neural Networks (D-CNNs) to detect face-morph Presentation Attacks (PAs). She is interested in working at the intersection of healthcare and artificial intelligence and will be pursuing an MD after graduation.

Alumni

JACQUELINE G. CAVAZOS (Ph.D. 2020) (Post-doctoral fellow at UC Irvine)

Jacqueline G. Cavazos

Jackie is a PhD student in the psychological sciences program. She received a BA in psychology from California State University, Fullerton where she focused on examining the effects of disguise and race on face recognition. Her current research focuses on the effects of race bias in face recognition identification in humans and face recognition algorithms and the potential strategies to reduce this effect. Some of these strategies include image presentation type (contiguous and distributed learning) and collaborative decision-making strategies. As part of a larger team, Jackie also works to design proficiency tests to examine forensic face examiner accuracy in challenging tasks.

Publications:

Cavazos, J.G., Phillips, P.J., Castillo, C. D. & O’Toole, A. J. (2020). IEEE Transactions on Biometrics, Behavior, and Identity Science. Vision IEEE: Transactions on Biometrics, Behavior & Identity Science.

Cavazos, J.G., Noyes, E., O’Toole, A. J. (2018). Learning context and the Other-Race Effect: Strategies for improving face recognition. Vision Research. doi:10.1016/j.visres.2018.03.003.

Phillips, P.J., Yates, A.N., Hu, Y., Hahn, C.A., Noyes, E., Jackson, K., Cavazos, J.G., Jeckeln, G., Ranjan, R., Sankaranarayanan, S., Chen, J-C., Castillo, C.D., Chellappa, R., White, D., & O’Toole, A.J. (2018). Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1721355115.

Jeckeln, G., Hahn, C.A., Noyes, E., Cavazos, J.G., & O’Toole, A. J. (2018). Wisdom of the social versus non-social crowd for face identification. British Journal of Psychology. doi:10.1111/bjop.12291.

Cavazos J. G., Jeckeln, G., Hu, Y., & O’Toole, A. J. (In Press). Strategies of face recognition by humans and machines. Deep Learning-based Face Analytics. Cambridge University Press.

Lab Affiliated Graduate Student

YOLANDA IVETTE COLON

Yolanda Ivette Colon

Ivette spent 4 years in the O’Toole Lab studying on deep convolutional neural networks for face recognition. She is now a PhD student at the University of Wisconsin- Madison, working under Dr. Emily Ward.

EILIDH NOYES

Eilidh Noyes

Eilidh is a lecturer at the University of Huddersfield in the UK.
Publication List on Google Scholar

CARINA A. HAHN, PHD

Carina A. Hahn

Carina is a scientist at the National Institute of Standards and Technology. See https://sites.google.com/view/carinahahn.

KELSEY JACKSON

Kelsey Jackson

RAHEL USMAN

Rahel Usman

FANG JIANG, PHD

Assistant Prof at U Nevada at Reno (See bio)

VAIDEHI NATU, PHD

Post-doctoral Fellow at Stanford (See bio)

ALLYSON RICE

DANA ROARK, PHD

Instructor in BBS at UT Dallas

Alumni with Dr. O'Toole

Dr. O’Toole with lab alumni