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	Records
	Author	Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez
	Title	Towards Real-Time Human Action Recognition			Type	Conference Article
	Year	2009	Publication	4th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	5524	Issue		Pages
	Keywords
	Abstract	This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art.
	Address	Póvoa de Varzim, Portugal
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-02171-8	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	ISE			Approved	no
	Call Number	DAG @ dag @ CBG2009			Serial	1215
Permanent link to this record



	Author	Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca
	Title	A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes			Type	Conference Article
	Year	2011	Publication	13th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	1776-1783
	Keywords
	Abstract	Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance.
	Address	Barcelona
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1550-5499	ISBN	978-1-4577-1101-5	Medium
	Area		Expedition		Conference	ICCV
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ CHM2011			Serial	1811
Permanent link to this record



	Author	Bhaskar Chakraborty; Ognjen Rudovic; Jordi Gonzalez
	Title	View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts			Type	Conference Article
	Year	2008	Publication	8th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Amsterdam; The Netherlands
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	FG
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ CRG2008			Serial	1113
Permanent link to this record



	Author	Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
	Title	Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics			Type	Conference Article
	Year	2014	Publication	1st Workshop on Computer Vision for Affective Computing	Abbreviated Journal
	Volume		Issue		Pages	1-8
	Keywords
	Abstract	Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression.
	Address	Singapore; November 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ACCV
	Notes	LAMP;			Approved	no
	Call Number	Admin @ si @ RBD2014			Serial	2599
Permanent link to this record



	Author	Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
	Title	Multi-observation Face Recognition in Videos based on Label Propagation			Type	Conference Article
	Year	2015	Publication	6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015	Abbreviated Journal
	Volume		Issue		Pages	10-17
	Keywords
	Abstract	In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods.
	Address	Boston; USA; June 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	LAMP; 600.068; 600.072;			Approved	no
	Call Number	Admin @ si @ RBD2015			Serial	2627
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Natural Facial Expression Recognition Using Dynamic and Static Schemes			Type	Conference Article
	Year	2009	Publication	5th International Symposium on Visual Computing	Abbreviated Journal
	Volume	5875	Issue		Pages	730–739
	Keywords
	Abstract	Affective computing is at the core of a new paradigm in HCI and AI represented by human-centered computing. Within this paradigm, it is expected that machines will be enabled with perceiving capabilities, making them aware about users’ affective state. The current paper addresses the problem of facial expression recognition from monocular videos sequences. We propose a dynamic facial expression recognition scheme, which is proven to be very efficient. Furthermore, it is conveniently compared with several static-based systems adopting different magnitude of facial expression. We provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). We also provide performance evaluations using arbitrary test video sequences.
	Address	Las Vegas, USA
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-10330-8	Medium
	Area		Expedition		Conference	ISVC
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaD2009			Serial	1257
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning			Type	Conference Article
	Year	2010	Publication	IEEE International Conference on Robotics and Automation	Abbreviated Journal
	Volume		Issue		Pages	156–161
	Keywords
	Abstract	In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.
	Address	Anchorage; AK; USA;
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1050-4729	ISBN	978-1-4244-5038-1	Medium
	Area		Expedition		Conference	ICRA
	Notes	OR; MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaD2010			Serial	1310
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Appearance-based Face Recognition Using A Supervised Manifold Learning Framework			Type	Conference Article
	Year	2012	Publication	IEEE Workshop on the Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	465-470
	Keywords
	Abstract	Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
	Address	Breckenridge; CO; USA
	Corporate Author				Thesis
	Publisher	IEEE Xplore	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1550-5790	ISBN	978-1-4673-0233-3	Medium
	Area		Expedition		Conference	WACV
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RaD2012d			Serial	1890
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Out-of-Sample Embedding by Sparse Representation			Type	Conference Article
	Year	2012	Publication	Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop	Abbreviated Journal
	Volume	7626	Issue		Pages	336-344
	Keywords
	Abstract	A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-34165-6	Medium
	Area		Expedition		Conference	SSPR&SPR
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RaD2012c			Serial	2175
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Pose-Invariant Face Recognition in Videos for Human-Machine Interaction			Type	Conference Article
	Year	2012	Publication	12th European Conference on Computer Vision	Abbreviated Journal
	Volume	7584	Issue		Pages	566.575
	Keywords
	Abstract	Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-33867-0	Medium
	Area		Expedition		Conference	ECCVW
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RaD2012e			Serial	2182
Permanent link to this record



	Author	Bogdan Raducanu; Jordi Vitria
	Title	Online Learning for Human-Robot Interaction			Type	Conference Article
	Year	2007	Publication	IEEE Conference on Computer Vision and Pattern Recognition Workshop on	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Minneapolis (USA)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPR
	Notes	OR; MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaV2007a			Serial	791
Permanent link to this record



	Author	Bogdan Raducanu; Jordi Vitria
	Title	Incremental Subspace Learning for Cognitive Visual Processes			Type	Conference Article
	Year	2007	Publication	Advances in Brain, Vision and Artificial Intelligence, 2nd International Symposium	Abbreviated Journal
	Volume	4729	Issue		Pages	214–223
	Keywords
	Abstract
	Address	Naples (Italy)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	BVAI’07
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaV2007b			Serial	901
Permanent link to this record



	Author	Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez
	Title	You are Fired! Nonverbal Role Analysis in Competitive Meetings			Type	Conference Article
	Year	2009	Publication	IEEE International Conference on Audio, Speech and Signal Processing	Abbreviated Journal
	Volume		Issue		Pages	1949–1952
	Keywords
	Abstract	This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
	Address	Taipei, Taiwan
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1520-6149	ISBN	978-1-4244-2353-8	Medium
	Area		Expedition		Conference	ICASSP
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RVG2009			Serial	1154
Permanent link to this record



	Author	Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta
	Title	Bag of Negatives for Siamese Architectures			Type	Conference Article
	Year	2019	Publication	30th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
	Address	Cardiff; United Kingdom; September 2019
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	BMVC
	Notes	CIC; 600.140; 600.118			Approved	no
	Call Number	Admin @ si @ GAB2019b			Serial	3263
Permanent link to this record



	Author	Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta
	Title	Area Under the ROC Curve Maximization for Metric Learning			Type	Conference Article
	Year	2022	Publication	CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition
	Abstract	Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.
	Address	New Orleans, USA; 20 June 2022
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	CIC; LAMP;			Approved	no
	Call Number	Admin @ si @ GAB2022			Serial	3700
Permanent link to this record