Ziga Emersic

  • Citations Per Year
Learn More
Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in(More)
Ear biometrics is gaining on popularity in recent years. One of the major problems in the domain is that there are no widely used, ear databases in the wild available. This makes comparison of existing ear recognition methods demanding and progress in the domain slower. Images that were taken under supervised conditions and are then used to train(More)
Dense descriptor-based feature extraction techniques represent a popular choice for implementing biometric ear recognition system and are in general considered to be the current state-of-the-art in this area. In this paper, we study the impact of various factors (i.e., head rotation, presence of occlusions, gender and ethnicity) on the performance of 8(More)
Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the overall processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear(More)
An increasing amount of video and image data is being shared between government entities and other relevant stakeholders and requires careful handling of personal information. A popular approach for privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still(More)
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and(More)
Ears are not subjected to facial expressions like faces are and do not require closer inspection like fingerprints do. However, there is a problem of occlusion, different lightning conditions and angles. These properties mean that the final outcome depends heavily on the selected database and classification procedures used in the evaluation process.(More)
  • 1