A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model

TL;DR

This paper introduces a deep cascaded fine-tuning CNN model for facial attractiveness prediction, achieving high correlation and highlighting the importance of facial features, lighting, and color information aligned with psychological insights.

Contribution

The paper presents a novel deep cascaded fine-tuning scheme with multi-channel face inputs, improving prediction accuracy and revealing key facial attributes influencing attractiveness.

Findings

Achieved a prediction correlation of 0.88.

Facial contours, eyes, and mouth are critical features.

Lighting and color information significantly impact attractiveness perception.

Abstract

This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image. With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88. This result convinces us that the problem of facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that were involved in facial beauty perception, which is consistent with the result of recent psychology studies. Furthermore, we analyze the high-level features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed. It is found that the contours and appearance of facial features, especially eyes and moth, are the most significant facial attributes for facial attractiveness prediction, which is also consistent with the visual perception intuition of human.