CS281B Project: Statistical Learning of Human Attractiveness
نویسنده
چکیده
We apply computer vision methods to the task of automatically predicting human attractiveness from frontal face images. A dataset of thousands of images and corresponding scores was obtained from a popular website that asks viewers to rate the attractiveness of the people appearing in the images. We applied several kernel regression techniques to the problem and found that while all techniques showed evidence of overfitting, partial least squares provided the best results on a test dataset. Overall statistical learning results are mediocre, but human performance isn’t much better.
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