Publications

@inproceedings{struc2015modest,

title = {Modest face recognition},

author = {Vitomir Štruc and Janez Križaj and Simon Dobrišek},

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

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

pages = {1--6},

publisher = {IEEE},

abstract = {The facial imagery usually at the disposal for forensics investigations is commonly of a poor quality due to the unconstrained settings in which it was acquired. The captured faces are typically non-frontal, partially occluded and of a low resolution, which makes the recognition task extremely difficult. In this paper we try to address this problem by presenting a novel framework for face recognition that combines diverse features sets (Gabor features, local binary patterns, local phase quantization features and pixel intensities), probabilistic linear discriminant analysis (PLDA) and data fusion based on linear logistic regression. With the proposed framework a matching score for the given pair of probe and target images is produced by applying PLDA on each of the four feature sets independently - producing a (partial) matching score for each of the PLDA-based feature vectors - and then combining the partial matching results at the score level to generate a single matching score for recognition. We make two main contributions in the paper: i) we introduce a novel framework for face recognition that relies on probabilistic MOdels of Diverse fEature SeTs (MODEST) to facilitate the recognition process and ii) benchmark it against the existing state-of-the-art. We demonstrate the feasibility of our MODEST framework on the FRGCv2 and PaSC databases and present comparative results with the state-of-the-art recognition techniques, which demonstrate the efficacy of our framework.},

keywords = {biometrics, face verification, Gabor features, image descriptors, LBP, multi modality, PaSC, performance evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICPR_Gajsek_2010,

title = {Multi-modal Emotion Recognition using Canonical Correlations and Acustic Features},

author = {Rok Gajšek and Vitomir Štruc and France Mihelič},

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

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR)},

pages = {4133-4136},

address = {Istanbul, Turkey},

organization = {IAPR},

abstract = {The information of the psycho-physical state of the subject is becoming a valuable addition to the modern audio or video recognition systems. As well as enabling a better user experience, it can also assist in superior recognition accuracy of the base system. In the article, we present our approach to multi-modal (audio-video) emotion recognition system. For audio sub-system, a feature set comprised of prosodic, spectral and cepstrum features is selected and support vector classifier is used to produce the scores for each emotional category. For video sub-system a novel approach is presented, which does not rely on the tracking of specific facial landmarks and thus, eliminates the problems usually caused, if the tracking algorithm fails at detecting the correct area. The system is evaluated on the eNTERFACE database and the recognition accuracy of our audio-video fusion is compared to the published results in the literature.},

keywords = {acustic features, canonical correlations, emotion recognition, facial expression recognition, multi modality, speech processing, speech technologies},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{TSD_Emo_Gajsek,

title = {Multi-modal Emotion Recognition based on the Decoupling of Emotion and Speaker Information},

author = {Rok Gajšek and Vitomir Štruc and France Mihelič},

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

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

booktitle = {Proceedings of Text, Speech and Dialogue (TSD)},

volume = {6231/2010},

pages = {275-282},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

series = {Lecture Notes on Computer Science},

abstract = {The standard features used in emotion recognition carry, besides the emotion related information, also cues about the speaker. This is expected, since the nature of emotionally colored speech is similar to the variations in the speech signal, caused by different speakers. Therefore, we present a gradient descent derived transformation for the decoupling of emotion and speaker information contained in the acoustic features. The Interspeech ’09 Emotion Challenge feature set is used as the baseline for the audio part. A similar procedure is employed on the video signal, where the nuisance attribute projection (NAP) is used to derive the transformation matrix, which contains information about the emotional state of the speaker. Ultimately, different NAP transformation matrices are compared using canonical correlations. The audio and video sub-systems are combined at the matching score level using different fusion techniques. The presented system is assessed on the publicly available eNTERFACE’05 database where significant improvements in the recognition performance are observed when compared to the stat-of-the-art baseline.},

keywords = {emotion recognition, facial expression recognition, multi modality, speech processing, speech technologies, spontaneous emotions, video processing},

pubstate = {published},

tppubtype = {inproceedings}

}