O'Toole, Alice J.

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6436

Alice O'Toole is the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences and fellow of the Association for Psychological Science (APS). She writes in the fields of psychology, neuroscience, and computational vision. She is also the Principal Investigator of the Face Perception Research Lab. Her research interests include perception, memory, and cognition, "with special interests in recognition memory for faces."

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    Social Trait Information in Deep Convolutional Neural Networks Trained for Face Identification
    (Wiley-Blackwell Publishing, 2019-05-27) Parde, Connor J.; Hu, Ying; Castillo, C.; Sankaranarayanan, S.; O'Toole, Alice J.; Parde, Connor J.; Hu, Ying; O'Toole, Alice J.
    Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social-trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human-assigned trait ratings from the 512 dimensional representations of faces that emerged at the top-layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top-level DCNN features produced by the network predicted the human-assigned social trait profiles with good accuracy. Human-assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training. ©2019 Cognitive Science Society, Inc.
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    Face Recognition Accuracy of Forensic Examiners, Superrecognizers, and Face Recognition Algorithms
    (National Academy of Sciences) Phillips, P. J.; Yates, A. N.; Hu, Ying; Hahn, Carina A.; Noyes, Eilidh; Jackson, Kelsey; Cavazos, Jacqueline G.; Jeckeln, Géraldine; Ranjan, R.; Sankaranarayanan, S.; Chen, J. -C; Castillo, C. D.; Chellappa, R.; White, D.; O'Toole, Alice J.; Hu, Ying; Hahn, Carina A.; Noyes, Eilidh; Jackson, Kelsey; Cavazos, Jacqueline G.; Jeckeln, Géraldine; O'Toole, Alice J.
    Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

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