Search Results for author: Vitomir Štruc

Found 19 papers, 2 papers with code

BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images

no code implementations3 May 2022 Darian Tomašević, Peter Peer, Vitomir Štruc

Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns.

Image Generation

A Global Modeling Approach for Load Forecasting in Distribution Networks

no code implementations1 Apr 2022 Miha Grabner, Yi Wang, Qingsong Wen, Boštjan Blažič, Vitomir Štruc

Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.

Load Forecasting

An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement

no code implementations24 Nov 2021 Dailé Osorio-Roig, Christian Rathgeb, Pawel Drozdowski, Philipp Terhörst, Vitomir Štruc, Christoph Busch

The attack is based on two observations: (1) to achieve high recognition accuracy, certain similarities between facial representations have to be retained in their privacy-enhanced versions; (2) highly similar facial representations usually originate from face images with similar soft-biometric attributes.

Dimensionality Reduction Face Recognition

Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection

no code implementations28 Sep 2021 Marija Ivanovska, Vitomir Štruc

Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficient anomaly detection across a diverse set of anomaly detection tasks.

Anomaly Detection Disentanglement

High Resolution Face Editing with Masked GAN Latent Code Optimization

1 code implementation20 Mar 2021 Martin Pernuš, Vitomir Štruc, Simon Dobrišek

The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i. e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially--selective treatment of local image areas.

Face Parsing

Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks

no code implementations28 Jan 2020 Jaka Šircelj, Tim Oblak, Klemen Grm, Uroš Petković, Aleš Jaklič, Peter Peer, Vitomir Štruc, Franc Solina

In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives.

Simultaneous regression and feature learning for facial landmarking

no code implementations24 Apr 2019 Janez Križaj, Peter Peer, Vitomir Štruc, Simon Dobrišek

We develop two distinct approaches around the proposed gating mechanism: i) the first uses a gated multiple ridge descent (GRID) mechanism in conjunction with established (hand-crafted) HOG features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses, ii) the second simultaneously learns multiple-descent directions as well as binary features (SMUF) that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing.

Face Alignment

Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study

no code implementations13 Apr 2019 Tim Oblak, Klemen Grm, Aleš Jaklič, Peter Peer, Vitomir Štruc, Franc Solina

It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings.

The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix

no code implementations11 Mar 2019 Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc

The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.

Person Recognition

Face Hallucination Revisited: An Exploratory Study on Dataset Bias

no code implementations21 Dec 2018 Klemen Grm, Martin Pernuš, Leo Cluzel, Walter Scheirer, Simon Dobrišek, Vitomir Štruc

This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle.

Face Hallucination

Face hallucination using cascaded super-resolution and identity priors

no code implementations28 May 2018 Klemen Grm, Simon Dobrišek, Walter J. Scheirer, Vitomir Štruc

In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors.

Face Hallucination Face Recognition +1

Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild

no code implementations27 Nov 2017 Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer

The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community.

Data Augmentation

Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations

1 code implementation4 Oct 2017 Klemen Grm, Vitomir Štruc, Anais Artiges, Matthieu Caron, Hazim Kemal Ekenel

However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce in the literature.

Face Recognition Face Verification

The Unconstrained Ear Recognition Challenge

no code implementations23 Aug 2017 Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer, Anjith George, Adil Ahmad, Elshibani Omar, Terrance E. Boult, Reza Safdari, Yuxiang Zhou, Stefanos Zafeiriou, Dogucan Yaman, Fevziye I. Eyiokur, Hazim K. Ekenel

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.

Person Recognition

Face Deidentification with Generative Deep Neural Networks

no code implementations28 Jul 2017 Blaž Meden, Refik Can Malli, Sebastjan Fabijan, Hazim Kemal Ekenel, Vitomir Štruc, Peter Peer

Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

no code implementations1 Feb 2017 Žiga Emeršič, Luka Lan Gabriel, Vitomir Štruc, Peter Peer

For our technique, we formulate the problem of ear detection as a two-class segmentation problem and train a convolutional encoder-decoder network based on the SegNet architecture to distinguish between image-pixels belonging to either the ear or the non-ear class.

Object Detection

Ear Recognition: More Than a Survey

no code implementations18 Nov 2016 Žiga Emeršič, Vitomir Štruc, Peter Peer

This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area.

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