The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

Authorship verification is a branch of forensic authorship analysis addressing the following task: Given a number of sample documents of an author A and a document allegedly written by A , the task is to decide whether the author of the latter document is truly A or not. We present a scalable authorship verification method that copes with this problem across different languages, genres and topics. The central concept of our method is a model, which is trained with Dutch, English, Greek, Spanish and German text documents. The model sets for each language specific parameters and a threshold that accepts or rejects the alleged author as A . The proposed method offers a wide range of benefits, e.g., a universal (static) threshold for each language and scalability regarding almost any involved component (classification function, ensemble strategy, features, etc.). Furthermore, the method benefits from low runtime due to the fact that no natural language processing techniques nor other computationally-intensive methods are involved. In our experiments, we applied the method on 28 test corpora including 4525 verification cases across 16 genres and a huge number of mixed topics, where we achieved competitive results (75% median accuracy). With these results we were able to outperform two state-of-the-art baselines, given the same training and test corpora

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