Dynamic multi-objective evolution of classifier ensembles for video face recognition

Connolly, Jean-François; Granger, Éric; Sabourin, Robert

doi:10.1016/j.asoc.2012.08.039

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…For example, artificial neural networks (i.e., systems that learn from data) have been used in different biometric applications involving pattern classification and identification (of a human (Dinkar andSambyal 2012, Melin et al 2012), of driver (Wu and Ye 2009), of finger-vein patterns (Wu and Liu 2011), of iris recognition (Sibai et al 2011), of human action (Youssef and Asari 2013), of gait (Zeng and Wang 2012), of the face (Connolly et al 2013;Kuo et al 2011;Choi et al 2012;Banerjee and Datta 2013;Lin and Lin 2013;Müller et al 2013), of the hand (Michael et al 2008), of the skin (Zaidan et al 2014), by keystroke (Uzun and Bicakci 2012) and by gesture, speech, handwritten text recognition and the like). Various biometric systems are being developed in such a manner (face recognition, fingerprint identification, hand geometry biometrics, retina scan, iris scan, signature, voice analysis, palm vein authentication and others).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…Although a limited amount of reference data is initially available during enrollment, new samples often become available over time, through re-enrollment, post analysis and labeling of operational data, etc. (Connolly et al 2013). Kuo et al (2011) propose an improved photometric stereo scheme based on improved kernel-independent component analysis method to reconstruct 3D human faces.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…Several passive approaches to ensemble adaptation, with varied ensemble generation, selection and fusion strategies have been proposed in the literature, adapting the fusion rule [2,17], the classifiers [8,6] or both at the same time [16]. Active approaches differ from the passive ones in their use of a change detection mechanism to drive ensemble adaptation.…”

Section: Thresholdingmentioning

confidence: 99%

Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams

Pagano

Granger

Sabourin

et al. 2014

Advanced Information Systems Engineering

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Adapting classification systems according to new input data streams raises several challenges in changing environments. Although several adaptive ensemble-based strategies have been proposed to preserve previously-acquired knowledge and reduce knowledge corruption, the fusion of multiple classifiers trained to represent different concepts can increase the uncertainty in prediction level, since only a sub-set of all classifier may be relevant. In this paper, a new score-level fusion technique, called Swavg h , is proposed where each classifier is dynamically weighted according to the similarity between an input pattern and the histogram representation of each concept present in the ensemble. During operations, the Hellinger distance between an input and the histogram representation of every previously-learned concept is computed, and the score of every classifier is weighted dynamically according to the resemblance to the underlying concept distribution. Simulation produced with synthetic problems indicate that the proposed fusion technique is able to increase system performance when input data streams incorporate abrupt concept changes, yet maintains a level of performance that is comparable to the average fusion rule when the changes are more gradual.

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“…Dynamic optimization methods methodically exploit and transfer useful knowledge from older environments and maintain adaptability to guide and speed up the exploration in emergent environments [17,18]. Some recent successful applications include products pricing [19], contaminant source characterization [20], vehicle routing [21], military mission planning [22], electric power supply optimization [23], video-based face recognition [24], and dynamic traveling salesman problem [25]. Despite this rich record of successful applications, however, dynamic optimization has not been employed to solve the crucial problem of dynamic water contamination emergency management yet.…”

Section: Introductionmentioning

confidence: 99%

A dynamic simulation–optimization model for adaptive management of urban water distribution system contamination threats

Rasekh

Brumbelow

2015

Applied Soft Computing

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Abstract. Urban water distribution systems hold a critical and strategic position in preserving public health and industrial growth. Despite the ubiquity of these urban systems, aging infrastructure, and increased risk of terrorism, decision support models for a timely and adaptive contamination emergency response still remain at an undeveloped stage. Emergency response is characterized as a progressive, interactive, and adaptive process that involves parallel activities of processing streaming information and executing response actions. This study develops a dynamic decision support model that adaptively is analyzed and demonstrated using a mid-size virtual city that resembles the dynamics and complexity of real-world urban systems. This adaptive emergency response optimization model is intended to be a major component of an all-inclusive cyberinfrastructure for efficient contamination threat management, which is currently under development.

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Dynamic multi-objective evolution of classifier ensembles for video face recognition

Cited by 15 publications

References 29 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams

A dynamic simulation–optimization model for adaptive management of urban water distribution system contamination threats

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