Facing Employers and Customers
| Autor: | Rémy Siegfried, Jean-Marc Odobez, Skanda Muralidhar, Daniel Gatica-Perez |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION facial expressions 02 engineering and technology first impressions dominance Multimodal interaction looking perceptions 0502 economics and business 0202 electrical engineering electronic engineering information engineering hirability Desk Facial expression Social computing behavior 05 social sciences Soft skills social computing hospitality cues Gaze job performance Job performance Eye tracking multimodal interaction 020201 artificial intelligence & image processing eye gaze 050203 business & management Cognitive psychology |
| Zdroj: | MUM |
| DOI: | 10.1145/3282894.3282925 |
| Popis: | Eye gaze and facial expressions are central to face-to-face social interactions. These behavioral cues and their connections to first impressions have been widely studied in psychology and computing literature, but limited to a single situation. Utilizing ubiquitous multimodal sensors coupled with advances in computer vision and machine learning, we investigate the connections between these behavioral cues and perceived soft skills in two diverse workplace situations (job interviews and reception desk). Pearson's correlation analysis shows a moderate connection between certain facial expressions, eye gaze cues and perceived soft skills in job interviews (r is an element of [-30,30]) and desk (r is an element of [20,36]) situations. Results of our computational framework to infer perceived soft skills indicates a low predictive power of eye gaze, facial expressions, and their combination in both interviews (R-2 is an element of [0.02,0.21]) and desk (R-2 is an element of [0.05, 0.15]) situations. Our work has important implications for employee training and behavioral feedback systems. |
| Databáze: | OpenAIRE |
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