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Title: Identity-Aware Deep Face Hallucination via Adversarial Face Verification
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IEEE International Conference on Biometrics Theory Applications and Systems
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Sponsoring Org:
National Science Foundation
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  1. Abstract

    Although still‐face effects are well‐studied, little is known about the degree to which the Face‐to‐Face/Still‐Face (FFSF) is associated with the production of intense affective displays. Duchenne smiling expresses more intense positive affect than non‐Duchenne smiling, while Duchenne cry‐faces express more intense negative affect than non‐Duchenne cry‐faces. Forty 4‐month‐old infants and their mothers completed the FFSF, and key affect‐indexing facial Action Units (AUs) were coded by expert Facial Action Coding System coders for the first 30 s of each FFSF episode. Computer vision software, automated facial affect recognition (AFAR), identified AUs for the entire 2‐min episodes. Expert coding and AFAR produced similar infant and mother Duchenne and non‐Duchenne FFSF effects, highlighting the convergent validity of automated measurement. Substantive AFAR analyses indicated that both infant Duchenne and non‐Duchenne smiling declined from the FF to the SF, but only Duchenne smiling increased from the SF to the RE. In similar fashion, the magnitude of mother Duchenne smiling changes over the FFSF were 2–4 times greater than non‐Duchenne smiling changes. Duchenne expressions appear to be a sensitive index of intense infant and mother affective valence that are accessible to automated measurement and may be a target for future FFSF research.

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  2. Abstract  
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  3. null (Ed.)
    People with superior face recognition have relatively thin cortex in face-selective brain areas, whereas those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) as abilities are acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultrahigh resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term “MR layers,” in the right fusiform face area (FFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, whereas vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) whereas the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of right FFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development. 
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