Qualitative Portrait Classification

Due to recent advances in high-quality digital photography, taking a large series of images is very inexpensive. Especially in portrait situations, this results in a possible advantage because subjects often feel uncomfortable during acquisition. Selecting from a larger set of images increases the chance of a more satisfying outcome. However, the selection process is not easy and time consuming as only a small number of images is typically considered as aesthetically pleasing. In this work, we propose a machine learning approach to mimic the selection process of a human subject. After a short training period, a large set of images can be classified instantly into two categories, good or bad. With the

proposed automatic pre-selection, the advantage of digital photography for portrait images is brought to a new level.