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17 results on '"Meriem Bendris"'

7. PERCOLI: A Person Identification System for the 2013 REPERE Challenge.

Author

Benoît Favre, Géraldine Damnati, Frédéric Béchet, Meriem Bendris, Delphine Charlet, Rémi Auguste, Stéphane Ayache, Benjamin Bigot, Alexandre Delteil, Richard Dufour, Corinne Fredouille, Georges Linarès, Jean Martinet, Grégory Senay, and Pierre Tirilly

Published
2013

12. Multimodal embedding fusion for robust speaker role recognition in video broadcast

Author

Frédéric Béchet, Sebastien Delecraz, Benoit Favre, Michael Rouvier, Meriem Bendris, Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Traitement Automatique du Langage Ecrit et Parlé (TALEP), Laboratoire d'Informatique et Systèmes (LIS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Modality (human–computer interaction), Modalities, Computer science, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Speaker recognition, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Image (mathematics), Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Speaker diarisation, Multimodal Speaker Embeddings, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Embedding, 020201 artificial intelligence & image processing, Speaker role recognition, [INFO]Computer Science [cs], Broadcast News
Abstract

International audience; Person role recognition in video broadcasts consists in classifying people into roles such as anchor, journalist, guest, etc. Existing approaches mostly consider one modality, either audio (speaker role recognition) or image (shot role recognition), firstly because of the non-synchrony between both modalities, and secondly because of the lack of a video corpus annotated in both modalities. Deep Neural Networks (DNN) approaches offer the ability to learn simultaneously feature representations (embeddings) and classification functions. This paper presents a multimodal fusion of audio, text and image embeddings spaces for speaker role recognition in asynchronous data. Monomodal embeddings are trained on exogenous data and fine-tuned using a DNN on 70 hours of French Broadcasts corpus for the target task. Experiments on the REPERE corpus show the benefit of the embeddings level fusion compared to the monomodal embeddings systems and to the standard late fusion method.

Published
2015

13. Multiple-view constrained clustering for unsupervised face identification in TV-broadcast

Author

Benoit Favre, Meriem Bendris, Rémi Auguste, Delphine Charlet, Géraldine Damnati, Laboratoire d'informatique Fondamentale de Marseille (LIF), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), France Télécom Recherche & Développement (FT R&D), France Télécom, Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), France Télécom Recherche et Développement [Lannion] (FTR&D), FOX MIIRE (LIFL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Traitement Automatique du Langage Ecrit et Parlé (TALEP), Laboratoire d'Informatique et Systèmes (LIS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Fuzzy clustering, Computer science, business.industry, Speech recognition, Constrained clustering, Pattern recognition, Speaker recognition, Facial recognition system, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Identification (information), ComputingMethodologies_PATTERNRECOGNITION, Face (geometry), Identity (object-oriented programming), Artificial intelligence, business, Cluster analysis
Abstract

International audience; Our goal is to automatically identify faces in TV broadcast without a pre-defined dictionary of identities. Most methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clustering. In TV content, people appear with many variations making the clustering difficult. In this case, speaker clustering can be a reliable link for face clustering. Multi-modal clustering methods assume a bipartite mapping between modalities. In this paper, we propose to build automatically an incomplete speaker-face mapping based on local evidence of OCR and Lip activity links. Then, we propose schemes of speaker constraints propagation to the face constrained-clustering problem. Experiments performed on the REPERE corpus show an improvement of face identification by propagating names to face clusters (+3.7% F-measure compared to the baseline).

Published
2014

14. Scene understanding for identifying persons in TV shows: beyond face authentication

Author

Géraldine Damnati, Delphine Charlet, Meriem Bendris, Benoit Favre, Mickael Rouvier, Favre, Benoit, Laboratoire d'informatique Fondamentale de Marseille (LIF), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), France Télécom Recherche & Développement (FT R&D), France Télécom, France Télécom Recherche et Développement [Lannion] (FTR&D), Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Traitement Automatique du Langage Ecrit et Parlé (TALEP), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), and Centre d'Enseignement et de Recherche en Informatique - CERI-Avignon Université (AU)

Subjects
Focus (computing), Authentication, Exploit, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Identification (information), [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Face (geometry), Three-dimensional face recognition, Computer vision, Artificial intelligence, Face detection, business, Baseline (configuration management), ComputingMilieux_MISCELLANEOUS
Abstract

Our goal is to automatically identify people in TV news and debates without any predefined dictionary of people. In this paper, we focus on the problem of person identification beyond face authentication in order to improve the identification results and not only where the face is detectable. We propose to use automatic scene analysis as features for people identification. We exploit two features: scene classification (studio and report) and camera identification. Then, people are identified by propagation strategies of overlaid names (OCR results) and speakers to scene classes and specific camera shots. Experiments performed on the REPERE corpus show improvement of face identification using scene understanding features (+13.9% of F-measure compared to the baseline).

Published
2014

15. Unsupervised Face Identification in TV Content using Audio-Visual Sources

Author

Benoit Favre, Rémi Auguste, Gregory Senay, Géraldine Damnati, Delphine Charlet, Meriem Bendris, Jean Martinet, Département Traitement du Signal et des Images (TSI), Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique Fondamentale de Marseille - UMR 6166 (LIF), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Centre National de la Recherche Scientifique (CNRS), Traitement Automatique du Langage Ecrit et Parlé (TALEP), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), France Télécom Recherche & Développement (FT R&D), France Télécom, France Télécom Recherche et Développement [Lannion] (FTR&D), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), FOX MIIRE (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Laboratoire Informatique d'Avignon (LIA), Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI, and Bilasco, Ioan Marius

Subjects
Computer science, business.industry, Speech recognition, Pattern recognition, Optical character recognition, [INFO] Computer Science [cs], computer.software_genre, Facial recognition system, Identification (information), Face (geometry), Identity (object-oriented programming), Three-dimensional face recognition, [INFO]Computer Science [cs], Artificial intelligence, Face detection, Cluster analysis, business, computer, ComputingMilieux_MISCELLANEOUS
Abstract

Our goal is to automatically identify faces in TV content without pre-defined dictionary of identities. Most of methods are based on identity detection (from OCR and ASR) and require a propagation strategy based on visual clusterings. In TV content, people appear with many variation making the clustering very difficult. In this case, identifying speakers can be a reliable link to identify faces. In this work, we propose to combine reliable unsupervised face and speaker identification systems through talking-faces detection in order to improve face identification results. First, OCR and ASR results are combined to extract locally the identities. Then, the reliable visual associations are used to propagate those identities locally. The reliable identified faces are used as unsupervised models to identify similar faces. Finally speaker identities are propagated to the faces in case of lip activity detection. Experiments performed on the REPERE database show an improvement of the recall of +5% compared to the baseline, without degrading the precision.

Published
2013

16. Talking faces indexing in TV-content

Author

Delphine Charlet, Gérard Chollet, and Meriem Bendris

Subjects
business.industry, Computer science, Search engine indexing, Pattern recognition, computer.software_genre, Hierarchical clustering, Visualization, Index (publishing), Face (geometry), Computer vision, Artificial intelligence, business, Cluster analysis, Face detection, Audio signal processing, computer
Abstract

Our objective is to index talking faces in a TV-Context: build a description of TV-content, in terms of talking people, without any pre-defined dictionary of identities. In TV-content, because of multi-face shots and non-speaking face shots, it is difficult to determine which face is speaking. In this work, a method is proposed which clusters people independently by the audio and by the visual information and combines these clusterings of people (audio and visual) in order to detect sequences of talking faces. The audio indexing system is based on agglomerative clustering with the Bayesian Information Criterion. The visual indexing system is based on costume detection and clustering of color histograms. The combination of both indexes is based on searching for the best match between both clusterings, to obtain a correspondence between the automatic audio labels and the automatic video labels. The talking faces are then determined by the intersection of the segments of the associated audio and video labels. Results of experiments on a TV-Show database show that a high correct detection rate can be achieved by the proposed method.

Published
2010

17. Introduction of quality measures in audio-visual identity verification

Author

Gérard Chollet, Delphine Charlet, and Meriem Bendris

Subjects
Contextual image classification, Computer science, business.industry, Speech recognition, Feature extraction, Pattern recognition, Acoustic testing, Signal-to-noise ratio, Principal component analysis, Entropy (information theory), Loudspeaker, Artificial intelligence, Mel-frequency cepstrum, Sound quality, business, Face detection
Abstract

Audiovisual identity verification exploits both image and audio information to improve the performance of the identification system. Unfortunately, both image and audio systems are sensitive to signal quality. In this paper, we propose a method to combine output classifiers based on both image and audio quality measures. We define classes of signal degradation within which we estimate the fusion weights and normalization parameters. Results of experiments on the BANCA database show that fusion using quality measures improves verification performance by 25% compared to the baseline fusion method.

Published
2009

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