Aesthetics Assessment of Images Containing Faces
This work addresses a domain-specific need for better aesthetic assessment in face images, which is incremental as it adapts existing methods to a focused application.
The paper tackles the problem of predicting aesthetic quality specifically for images containing human faces, which are prevalent online, and demonstrates that their method outperforms existing approaches on four databases for both binary and continuous score prediction.
Recent research has widely explored the problem of aesthetics assessment of images with generic content. However, few approaches have been specifically designed to predict the aesthetic quality of images containing human faces, which make up a massive portion of photos in the web. This paper introduces a method for aesthetic quality assessment of images with faces. We exploit three different Convolutional Neural Networks 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 databases in the state-of-the-art.