Name
Abstract | ||
|---|---|---|
We explore how CERT [15], a computer expression recognition toolbox trained on a large dataset of spontaneous facial expressions (FFD07), generalizes to a new, previously unseen dataset (FERA). The experiment was unique in that the authors had no access to the test labels, which were guarded as part of the FERA challenge. We show that without any training or special adaptation to the new database, CERT performs better than a baseline method trained exclusively on that database. Best results are achieved by retraining CERT with a combination of old and new data. We also found that the FERA dataset may be too small and idiosyncratic to generalize to other datasets. Training on FERA alone produced good results on FERA but very poor results on FFD07. We reflect on the importance of challenges like this for the future of the field, and discuss suggestions for standardization of future challenges. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FG.2011.5771369 | FG |
Keywords | Field | DocType |
face,gold,face recognition,support vector machine,databases,detectors,support vector machines,facial expression,pipelines | Facial recognition system,Facial expression recognition,Emotion recognition,Computer science,Support vector machine,Toolbox,Facial expression,Artificial intelligence,Standardization,Machine learning,Retraining | Conference |
Citations | PageRank | References |
13 | 0.77 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingfan Wu | 1 | 1382 | 85.10 |
| Nicholas J. Butko | 2 | 140 | 10.70 |
| Paul Ruvolo | 3 | 486 | 29.52 |
| Jacob Whitehill | 4 | 988 | 58.75 |
| Marian Stewart Bartlett | 5 | 2026 | 183.92 |
| Javier R. Movellan | 6 | 1853 | 150.44 |