Impact of Zika Virus Emergence in French Guiana: A Large General Population Seroprevalence Survey.

BACKGROUND: Since the identification of Zika virus (ZIKV) in Brazil in May 2015, the virus has spread throughout the Americas. However, ZIKV burden in the general population in affected countries remains unknown. METHODS: We conducted a general population survey in the different communities of French Guiana through individual interviews and serologic survey during June-October 2017. All serum samples were tested for anti-ZIKV immunoglobulin G antibodies using a recombinant antigen-based SGERPAxMap microsphere immunoassay, and some of them were further evaluated through anti-ZIKV microneutralization tests. RESULTS: The overall seroprevalence was estimated at 23.3% (95% confidence interval [CI], 20.9%-25.9%) among 2697 participants, varying from 0% to 45.6% according to municipalities. ZIKV circulated in a large majority of French Guiana but not in the most isolated forest areas. The proportion of reported symptomatic Zika infection was estimated at 25.5% (95% CI, 20.3%-31.4%) in individuals who tested positive for ZIKV. CONCLUSIONS: This study described a large-scale representative ZIKV seroprevalence study in South America from the recent 2015-2016 Zika epidemic. Our findings reveal that the majority of the population remains susceptible to ZIKV, which could potentially allow future reintroductions of the virus

Multi-output random forests for facial action unit detection

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Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection

International audienceFully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression Predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems

Dynamic pose-robust facial expression recognition by multi-view pairwise conditional random forests

International audienc

Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection

International audienceFully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by the training data. In this work, we propose to train Random Forests upon spatially defined local subspaces of the face. The output local predictions form a categorical expression-driven high-level representation that we call Local Expression Predictions (LEPs). LEPs can be combined to describe categorical facial expressions as well as Action Units (AUs). Furthermore, LEPs can be weighted by confidence scores provided by an autoencoder network. Such network is trained to locally capture the manifold of the non-occluded training data in a hierarchical way. Extensive experiments show that the proposed LEP representation yields high descriptive power for categorical expressions and AU occurrence prediction, and leads to interesting perspectives towards the design of occlusion-robust and confidence-aware FER systems