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    Affectiva Facial Coding SDK
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Science Resources

This page features scientific publications and posters created by our in-house scientists. The content is a valuable resource showcasing our team’s research and expertise in various fields, and provides a foundation for evaluating the technologies behind our research software.

  • Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In-the-Wild”

  • Predicting Online Media Effectiveness Based on Smile Responses Gathered Over the Internet

  • Choose Settings Carefully: Comparing Action Unit Detection At Different Settings Using A Large-Scale Dataset

  • Predicting Ad Liking and Purchase Intent: Large-scale Analysis of Facial Responses to Ads

  • Affectiva SDK: A Cross-Platform RealTime Multi-Face Expression Recognition Toolkit

  • Aurora Performance Report for Practical Use Case Scenario

  • Yarbus in the age of Webcam Eye-Tracking

  • Eye-tracking capabilities in evaluating human factors during cognitive and motor tasks

  • Multimodal biometric data collection of interpersonal communication

  • WebET 3.0 – Validation Study

  • Systematic Evaluation of Driver’s Behavior: A Multimodal Biometric Study

  • iMotions Research Report 2024

  • Which CNNs and Training Settings to Choose for Action Unit Detection? A Study Based on a Large-Scale Dataset

  • Automatic Detection of Sentimentality from Facial Expressions

  • AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit

  • iMotions Research Report 2023

  • iMotions WebET 3.0 White Paper

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