Publications

@article{TextEmotionESWA,

title = {DHF-Net: A hierarchical feature interactive fusion network for dialogue emotion recognition},

author = {Chenquan Gan and Yucheng Yang and Qingyi Zhub and Deepak Kumar Jain and Vitomir Struc},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422016025?via%3Dihub},

doi = {https://doi.org/10.1016/j.eswa.2022.118525},

year  = {2022},

date = {2022-12-30},

urldate = {2022-08-01},

journal = {Expert Systems with Applications},

volume = {210},

abstract = {To balance the trade-off between contextual information and fine-grained information in identifying specific emotions during a dialogue and combine the interaction of hierarchical feature related information, this paper proposes a hierarchical feature interactive fusion network (named DHF-Net), which not only can retain the integrity of the context sequence information but also can extract more fine-grained information. To obtain a deep semantic information, DHF-Net processes the task of recognizing dialogue emotion and dialogue act/intent separately, and then learns the cross-impact of two tasks through collaborative attention. Also, a bidirectional gate recurrent unit (Bi-GRU) connected hybrid convolutional neural network (CNN) group method is designed, by which the sequence information is smoothly sent to the multi-level local information layers for feature exaction. Experimental results show that, on two open session datasets, the performance of DHF-Net is improved by 1.8% and 1.2%, respectively.},

keywords = {attention, CNN, deep learning, dialogue, emotion recognition, fusion, fusion network, nlp, semantics, text, text processing},

pubstate = {published},

tppubtype = {article}

}

@article{peer2014strategies,

title = {Strategies for exploiting independent cloud implementations of biometric experts in multibiometric scenarios},

author = {Peter Peer and Žiga Emeršič and Jernej Bule and Jerneja Žganec-Gros and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/en/strategiesforexploitingindependentcloudimplementationsofbiometricexpertsinmultibiometricscenarios/},

doi = {http://dx.doi.org/10.1155/2014/585139},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Mathematical problems in engineering},

volume = {2014},

publisher = {Hindawi Publishing Corporation},

abstract = {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 of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.},

keywords = {application, biometrics, cloud computing, face recognition, fingerprint recognition, fusion},

pubstate = {published},

tppubtype = {article}

}

@article{emersic2014case,

title = {A case study on multi-modal biometrics in the cloud},

author = {Žiga Emeršič and Jernej Bule and Jerneja Žganec-Gros and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/en/acasestudyonmulti-modalbiometricsinthecloud/},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Electrotechnical Review},

volume = {81},

number = {3},

pages = {74},

publisher = {Elektrotehniski Vestnik},

abstract = {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 from the perspective of performance gains, training complexity and resource consumption and discuss the results of our analysis. The experimental evaluation is conducted based on two biometric cloud-services developed in the scope of the Competence Centere CLASS, a face recognition service and a fingerprint recognition service, which are also briefly described in the paper. The presented results are important to researchers and developers working in the area of biometric services for the cloud looking for easy solutions for improving the quality of their services.},

keywords = {cloud, cloud computing, face recognition, face verification, fingerprint verification, fingerprints, fusion},

pubstate = {published},

tppubtype = {article}

}

@article{EV_Struc_2013,

title = {Zlivanje informacij za zanseljivo in robustno razpoznavanje obrazov},

author = {Vitomir Štruc and Jerneja Žganec-Gros and Nikola Pavešić and Simon Dobrišek},

url = {https://lmi.fe.uni-lj.si/en/zlivanjeinformacijzazanseljivoinrobustnorazpoznavanjeobrazov/},

year  = {2013},

date = {2013-09-01},

urldate = {2013-09-01},

journal = {Electrotechnical Review},

volume = {80},

number = {3},

pages = {1-12},

abstract = {The existing face recognition technology has reached a performance level where it is possible to deploy it in various applications providing they are capable of ensuring controlled conditions for the image acquisition procedure. However, the technology still struggles with its recognition performance when deployed in uncontrolled and unconstrained conditions. In this paper, we present a novel approach to face recognition designed specifically for these challenging conditions. The proposed approach exploits information fusion to achieve robustness. In the first step, the approach crops the facial region from each input image in three different ways. It then maps each of the three crops into one of four color representations and finally extracts several feature types from each of the twelve facial representations. The described procedure results in a total of thirty facial representations that are combined at the matching score level using a fusion approach based on linear logistic regression (LLR) to arrive at a robust decision regarding the identity of the subject depicted in the input face image. The presented approach was enlisted as a representative of the University of Ljubljana and Alpineon d.o.o. to the 2013 face-recognition competition that was held in conjunction with the IAPR International Conference on Biometrics and achieved the best overall recognition results among all competition participants. Here, we describe the basic characteristics of the approach, elaborate on the results of the competition and, most importantly, present some interesting findings made during our development work that are also of relevance to the research community working in the field of face recognition.},

keywords = {biometrics, face recognition, fusion, performance evaluation},

pubstate = {published},

tppubtype = {article}

}