Department of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336, 20126 Milano, Italy.
Sensors (Basel). 2021 Feb 12;21(4):1307. doi: 10.3390/s21041307.
The automatic assessment of the aesthetic quality of a photo is a challenging and extensively studied problem. Most of the existing works focus on the aesthetic quality assessment of photos regardless of the depicted subject and mainly use features extracted from the entire image. It has been observed that the performance of generic content aesthetic assessment methods significantly decreases when it comes to images depicting faces. This paper introduces a method for evaluating the aesthetic quality of images with faces by encoding both the properties of the entire image and specific aspects of the face. Three different convolutional neural networks are exploited to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e., low/high, and continuous aesthetic score prediction on four different image databases in the state-of-the-art.
自动评估照片的美学质量是一个具有挑战性且被广泛研究的问题。现有的大多数作品都专注于不考虑所描绘主题的照片美学质量评估，并且主要使用从整个图像中提取的特征。已经观察到，当涉及到描绘人脸的图像时，通用内容美学评估方法的性能会显著下降。本文提出了一种通过编码整个图像的属性和人脸的特定方面来评估带有人脸的图像美学质量的方法。利用三个不同的卷积神经网络来编码有关感知质量、全局图像美学和面部属性的信息；然后，训练一个模型来组合这些特征，以明确预测包含人脸的图像的美学。实验结果表明，我们的方法在四个不同的图像数据库中，无论是在二进制（即低/高）还是连续美学评分预测方面，都优于现有方法。
