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      SuperpowerGlass : A Wearable Aid for the At-Home Therapy of Children with Autism

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          The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression

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            Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies.

            This article reviews behavioral and electrophysiological studies of face processing and discusses hypotheses for understanding the nature of face processing impairments in autism. Based on results of behavioral studies, this study demonstrates that individuals with autism have impaired face discrimination and recognition and use atypical strategies for processing faces characterized by reduced attention to the eyes and piecemeal rather than configural strategies. Based on results of electrophysiological studies, this article concludes that face processing impairments are present early in autism, by 3 years of age. Such studies have detected abnormalities in both early (N170 reflecting structural encoding) and late (NC reflecting recognition memory) stages of face processing. Event-related potential studies of young children and adults with autism have found slower speed of processing of faces, a failure to show the expected speed advantage of processing faces versus nonface stimuli, and atypical scalp topography suggesting abnormal cortical specialization for face processing. Other electrophysiological studies have suggested that autism is associated with early and late stage processing impairments of facial expressions of emotion (fear) and decreased perceptual binding as reflected in reduced gamma during face processing. This article describes two types of hypotheses-cognitive/perceptual and motivational/affective--that offer frameworks for understanding the nature of face processing impairments in autism. This article discusses implications for intervention.
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              Enhanced local texture feature sets for face recognition under difficult lighting conditions.

              Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources--Gabor wavelets and LBP--showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.
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                Author and article information

                Journal
                Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
                Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
                Association for Computing Machinery (ACM)
                2474-9567
                2474-9567
                September 11 2017
                September 11 2017
                : 1
                : 3
                : 1-22
                Affiliations
                [1 ]Stanford University
                Article
                10.1145/3130977
                44b2ba10-9951-4925-ab7a-8758cc3ef40e
                © 2017

                http://www.acm.org/publications/policies/copyright_policy#Background

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