FaceMemNet: Predicting Face Memorability with Deep Neural Networks

Abstract With the advent of social media in our day to day life, we are exposed to plenty of images, especially face photographs, every day. Recent behavioural studies have shown that some of these photographs stick in the mind better than others. However, it is still unclear what factors make a picture more or less memorable. Previous research have shown that memorability is an intrinsic property of an image, hence the memorability of an image can be computed from that image. Moreover, various works found that the memorability of an image is highly consistent across people and also over time. Recently, researchers employed deep neural networks to predict image memorability. Here, we show although those models perform well on scene and object images, they perform poorly on photographs of human faces. We demonstrate and explain why generic memorability models do not result in an acceptable performance on face photographs and propose seven different models to estimate the memorability of face images. In addition, we show that these models outperform the previous classical methods, which were used for predicting face memorability.