期刊
ACM COMPUTING SURVEYS
卷 47, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2682899
关键词
Measurement; Performance Affective computing; human-centered computing; evaluation; methodology; survey
资金
- National Science Foundation (NSF) [ITR 0325428, HCC 0834847, DRL 1235958, 1122374]
- Bill & Melinda Gates Foundation
- Direct For Education and Human Resources
- Division Of Research On Learning [1235958] Funding Source: National Science Foundation
Affect detection is an important pattern recognition problem that has inspired researchers from several areas. The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results. This article provides such a survey via a quantitative review and meta-analysis of 90 peer-reviewed MM systems. The review indicated that the state of the art mainly consists of person-dependent models (62.2% of systems) that fuse audio and visual (55.6%) information to detect acted (52.2%) expressions of basic emotions and simple dimensions of arousal and valence (64.5%) with feature-(38.9%) and decision-level (35.6%) fusion techniques. However, there were also person-independent systems that considered additional modalities to detect nonbasic emotions and complex dimensions using model-level fusion techniques. The meta-analysis revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%). However, improvements were three times lower when systems were trained on natural (4.59%) versus acted data (12.7%). Importantly, MM accuracy could be accurately predicted (cross-validated R-2 of 0.803) from unimodal accuracies and two system-level factors. Theoretical and applied implications and recommendations are discussed.
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