Žiga Emeršič

Žiga Emeršič
University of Ljubljana · Department of Artificial Intelligence

About

44
Publications
49,317
Reads
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912
Citations
Citations since 2016
40 Research Items
910 Citations
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Introduction

Publications

Publications (44)
Article
Full-text available
Crowd counting has a range of applications and it is an important task that can help with the accident prevention such as crowd crushes and stampedes in political protests, concerts, sports, and other social events. Many crown counting approaches have been proposed in the recent years. In this paper we compare five deep-learning-based approaches to...
Conference Paper
UWB-based positioning systems have been proven to provide a significant high level of accuracy hence offering a huge potential for a variety of indoor applications. However, the major challenges related to UWB localization are multipath effects, excess delay, clock drift, signal interferences and system computational time to estimate the user posit...
Article
Full-text available
>>> Springer Nature SharedIt initiative publicly shares a full-text view-only version of the paper by using the link https://rdcu.be/Os7a! >>> Ear recognition technology has long been dominated by (local) descriptor-based techniques due to their formidable recognition performance and robustness to various sources of image variability. While deep-le...
Chapter
In this chapter we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for...
Chapter
This chapter introduces COM-Ear, a deep constellation model for ear recognition. Different from competing solutions, COM-Ear encodes global as well as local characteristics of ear images and generates descriptive ear representations that ensure competitive recognition performance. The model is designed as dual-path convolutional neural network (CNN...
Conference Paper
Ear recognition has seen steady development in the recent years. Despite numerous novel approaches ranging from traditional approaches based on local feature extraction to deep learning approaches, certain issues still remain unsolved. As pointed out in recent studies, one of the most prominent issues is the problem of ear alignment. To tackle this...
Article
Full-text available
Purpose: The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field. Met...
Conference Paper
Full-text available
This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challen...
Preprint
Full-text available
This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challen...
Chapter
Ear recognition has seen multiple improvements in recent years and still remains very active today. However, it has been approached from recognition and detection perspective separately. Furthermore, deep-learning-based approaches that are popular in other domains have seen limited use in ear recognition and even more so in ear detection. Moreover,...
Article
Full-text available
In this work we analyze the impact of denoising, contrast and edge enhancement using the Deceived Non Local Means (DNLM) filter in a Convolutional Neural Network (CNN) based approach for age estimation using digital X-ray images from hands. The DNLM filter contains two parameters which control edge enhancement and denoising. Increasing levels were...
Article
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 processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detecti...
Article
Full-text available
Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individ...
Article
Full-text available
Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains. In contrast to other biometric modalities...
Conference Paper
Full-text available
Among the different biometric traits that can be used for person recognition, the human iris is generally considered to be among the most accurate. However, despite a plethora of desirable characteristics, iris recognition is not widely as widely used as competing biometric modalities likely due to the high cost of existing commercial iris-recognit...
Conference Paper
Full-text available
IoT has seen steady growth over recent years – smart home appliances, smart personal gear, personal assistants and many more. The same is true for the field of bio-metrics where the need for automatic and secure recognition schemes have spurred the development of fingerprint-and face-recognition mechanisms found today in most smart phones and simil...
Article
Full-text available
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 dat...
Article
Full-text available
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 m...
Article
Full-text available
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...
Conference Paper
Full-text available
Ear as a biometric modality presents a viable source for automatic human recognition. In recent years local description methods have been gaining on popularity due to their invariance to illumination and occlusion. However, these methods require that images are well aligned and preprocessed as good as possible. This causes one of the greatest chall...
Conference Paper
Full-text available
In the last decade person recognition based on various biometric metrics have steadily been gaining on popularity. The same holds for machine learning approaches and various image classification and retrieval techniques. However, many techniques rely on distinguishing between significantly dissimilar images, which is often not the case in person re...
Article
Full-text available
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 c...
Article
Full-text available
Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation...
Article
Full-text available
Cloud computing is particularly interesting for the area of biometric recognition, where scalability, availability and accessibility are important aspects. In this paper we try to evaluate different strategies for combining existing uni-modal (cloud-based) biometric experts into a multi-biometric cloud-service. We analyze several fusion strategies...

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Projects

Projects (2)
Project
Tackling a 2D ear biometrics using images captured in uncontrolled environments.