A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

Exploiting object dynamics for recognition and control

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 127-132).This thesis explores how state-of-the-art object recognition methods can benefit from integrating information across multiple observations of an object. Considered are active vision systems that allow to steer the camera along predetermined trajectories, resulting in sweeps of ordered views of an object. For systems of this kind, a solution is presented that exploits the order relationship between successive frames to derive a classifier based on the characteristic motion of local features across the sweep. It is shown that this motion model reveals structural information about the object that can be exploited for recognition. The main contribution of this thesis is a recognition system that extends invariant local features (shape context) into the time domain by adding the mentioned feature motion model into a joint classifier. Second, an entropy-based view selection scheme is presented that allows the vision system to skip ahead to highly discriminative viewing positions. Using two datasets, one standard (ETH-80) and one collected from our robot head, both feature motion and active view selection extensions are shown to achieve a higher-quality hypothesis about the presented object quicker than a baseline system treating object views as an unordered stream of images.by Philipp Robbel.S.M

Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey

While Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings, they are affected by some crucial weaknesses. On the one hand, DNNs depend on exploiting a vast amount of training data, whose labeling process is time-consuming and expensive. On the other hand, DNNs are often treated as black box systems, which complicates their evaluation and validation. Both problems can be mitigated by incorporating prior knowledge into the DNN. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve. This promises an increased data-efficiency and filter responses that are interpretable more easily. In this survey, we try to give a concise overview about different approaches to incorporate geometrical prior knowledge into DNNs. Additionally, we try to connect those methods to the field of 3D object detection for autonomous driving, where we expect promising results applying those methods.Comment: Survey Pape