Assessing the attractiveness of human face based on machine learning

Adriana Żejmo; Maciej Gielert; Marcin Grabski; Bożena Kostek

Assessing the attractiveness of human face based on machine learning

Abstrakt

The attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing the algorithms, i.e., a dataset named SCUT-FBP5500 to train the deep learning models to predict facial attractiveness and Face Research Lab London Set designated for the test. The first model pays attention to the dominant background colors in the photo; the second model is based on a pre-trained deep neural network. Finally, for facial proportion assessment, distances between key points on the face are linked with attractiveness ratings, so the last dataset considers face proportions. Several algorithms are trained and tested, including baseline machine learning algorithms, i.e., LinearSVR, SDGRegressor, Lasso, RandomForestRegressor, and deep models, such as Xception VGG19 ResNet50v2, and MobileNetv2. A discussion of the results, as well as some concluding remarks, are also provided. The results from the trained models based on SCUT-FBP5500 show a systematic error for the Face Research Lab London Set database. This is probably caused by a different type of image evaluation in both databases. Although the results obtained show no visible winner among the algorithms employed, the best results are seen for five clusters and five colors fed onto the regressor.