Age, Gender & Emotion Benchmarks

Real Age Estimation

This table shows the mean absolute error for our methods along with competitive approaches. Our method outperforms the second best method by 1.58 years.

Methods MAE
Sighthound 5.76
Rothe et al. [1] 7.34
Microsoft [2] 7.62
Face++ [3] 11.04

Age Classification Accuracy of Group Dataset

We report both the exact accuracy as well as the 1-off accuracy.

Methods top1 1-off
Sighthound 70.5% 96.2%
Hou et al. [4] 65.0% 96.1%
Rothe et al. [1] 62.3% 94.3%
Dong et al. [5] 56.0% 92.0%
Gallegher et al. [6] 42.9% 78.1%

Emotion Recognition Accuracy on Sighthound Dataset

Methods Accuracy
Sighthound 76.1%
Microsoft [2] 61.3%

Gender Recognition Accuracy on the Adience Benchmark

Methods Accuracy
Sighthound 91.00%
Microsoft [2] 90.86%
Rothe et al. [1] 88.75%
Levi and Hassner [7] 86.80%
Kairos [8] 84.66%
Face++ [3] 83.04%
References
1. Rothe, R., Radu Timofte, L.V.G.: Dex: Deep expectation of apparent age from asingle image. In: International Conference on Computer Vision (ICCV). (2015)
2. Microsoft-Face-API.: (https://www.microsoft.com/cognitive-services/en-us/face-api.)
3. Face++.: (http://old.faceplusplus.com/demo-detect/)
4. Hou, L., Yu, C.P., Samaras., D.: Squared earth mover’s distance-based loss fortraining deep neural networks. In: arXiv. (2016)
5. Yuan, D., Liu, Y., Lian., S.: Automatic age estimation based on deep learningalgorithm. In: Neurocomputing. (2016)
6. Gallagher, A.C., Chen., T.: Understanding images of groups of people. In: CVPR.(2009)
7. Levi, G., Hassner., T.: Age and gender classification using convolutional neural networks. In: CVPRW. (2015)
8. Kairos.: (https://www.kairos.com/kairos-2.0/demos)