Master's Thesis: Detection of Suggestive Poses and Facial Expressions

Background/Motivation: For the detection of erotic and pornographic images, models that can recognise human skin, body parts, or scenes are often used. With the help of appropriate datasets [1], classification and object detection models can be trained. However, there are also images that are obviously erotic or pornographic, but cannot be recognised by conventional methods. This applies, for example, to images in which erotic poses or partial sections of pornographic scenes can be recognised. Here, poses and facial expressions can be used for classification, among other things [2]. Objective: The aim of this master's thesis is to investigate whether and to what extent poses and facial expressions can be used to recognise erotic and pornographic imagery. First, it should be researched which existing approaches are suitable for addressing the question. Gaps in existing datasets and models should be described and filled with our own data and models. Based on the developed methods, it should then be evaluated whether (1) reliable detection of erotic poses and facial expressions is possible and (2) whether erotic and pornographic images can be distinguished from other categories based on the detected poses and facial expressions. In this context, different counter classes should be evaluated, such as everyday or sports images. Results: As part of this master's thesis, the following results are to be achieved: • Dataset with annotations for the recognition of erotic poses and facial expressions. • Implementation of new approaches for the classification of poses and facial expressions, both in everyday situations and in erotic and pornographic contexts. • Evaluation of the models, both regarding the classification of poses and facial expressions as well as the classification of images (pornographic/erotic/normal).