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Recognition of human faces attractiveness (SCUT-FBP dataset) in Python
beauty_vision
Recognition of human faces attractiveness (the SCUT-FBP dataset [1])
Overview
The SCUT-FBP dataset [1] contains 500 samples (images), for each image there is rating in the range (1,5) measuring beauty of an Asian female face.
Support Vector Regression (SVR) is trained on top of different features (in some cases projected by PCA with 50 components). Average Pearson correlation (PC) for 5 independent 10-fold cross validation tests is reported as in [1].
In all experiments images are first resized to (224,294), then central crop (224,224) is taken.
Results
| Model | Code | Avg PC for 5 tests |
|---|---|---|
| Combined features + PCA + SVR [1] | - | 0.6433 |
| ConvNet [1] | - | 0.8187 |
| 16 random filters + PCA50+ rbf SVR | [beauty_baseline_random] (beauty_baseline_random.py) | 0.642 |
| 16 random filters + linear SVR | [beauty_baseline_random] (beauty_baseline_random.py) | 0.646 |
| 24 random filters + linear SVR | [beauty_baseline_random] (beauty_baseline_random.py) | 0.660 |
| 24 Gabor filters + PCA50+ rbf SVR | [beauty_baseline_gabors] (beauty_baseline_gabors.py) | 0.638 |
| 24 colored Gabor filters + PCA50 + rbf SVR | [beauty_baseline_gabors] (beauty_baseline_gabors.py) | 0.614 |
| Vgg-ImageNet (pool5+fc6) [2] + linear SVR | [beauty_vgg_imagenet] (beauty_vgg_imagenet.py) | 0.804 |
| Vgg-Face (pool5+fc6) [3] + linear SVR | [beauty_vgg_face] (beauty_vgg_face.py) | 0.856 |
Example of prediction
References
[1] Xie, Duorui, Lingyu Liang, Lianwen Jin, Jie Xu, and Mengru Li. "SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception." In Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, pp. 1821-1826. IEEE, 2015.
[2] https://gist.github.com/ksimonyan/fd8800eeb36e276cd6f9
[3] http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
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