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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

vgg_face_prediction_example

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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Recognition of human faces attractiveness (SCUT-FBP dataset) in Python

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