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ILRA: Novelty Detection in Face-Based Intervener Re-Identification

Instituto Universitario SIANI, Universidad de Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas, Spain
Department of Computer Science and Artificial Intelligence, UPV-EHU, 20018 Gipuzkoa, Spain
Statistics Section: Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, 08028 Barcelona, Spain
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2019, 11(9), 1154;
Received: 22 July 2019 / Revised: 4 September 2019 / Accepted: 8 September 2019 / Published: 11 September 2019
Transparency laws facilitate citizens to monitor the activities of political representatives. In this sense, automatic or manual diarization of parliamentary sessions is required, the latter being time consuming. In the present work, this problem is addressed as a person re-identification problem. Re-identification is defined as the process of matching individuals under different camera views. This paper, in particular, deals with open world person re-identification scenarios, where the captured probe in one camera is not always present in the gallery collected in another one, i.e., determining whether the probe belongs to a novel identity or not. This procedure is mandatory before matching the identity. In most cases, novelty detection is tackled applying a threshold founded in a linear separation of the identities. We propose a threshold-less approach to solve the novelty detection problem, which is based on a one-class classifier and therefore it does not need any user defined threshold. Unlike other approaches that combine audio-visual features, an Isometric LogRatio transformation of a posteriori (ILRA) probabilities is applied to local and deep computed descriptors extracted from the face, which exhibits symmetry and can be exploited in the re-identification process unlike audio streams. These features are used to train the one-class classifier to detect the novelty of the individual. The proposal is evaluated in real parliamentary session recordings that exhibit challenging variations in terms of pose and location of the interveners. The experimental evaluation explores different configuration sets where our system achieves significant improvement on the given scenario, obtaining an average F measure of 71.29% for online analyzed videos. In addition, ILRA performs better than face descriptors used in recent face-based closed world recognition approaches, achieving an average improvement of 1.6% with respect to a deep descriptor. View Full-Text
Keywords: re-identification; open world scenario; novelty detection; one-class classification; ILR transformation; local descriptors; deep descriptor re-identification; open world scenario; novelty detection; one-class classification; ILR transformation; local descriptors; deep descriptor
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Marín-Reyes, P.A.; Irigoien, I.; Sierra, B.; Lorenzo-Navarro, J.; Castrillón-Santana, M.; Arenas, C. ILRA: Novelty Detection in Face-Based Intervener Re-Identification. Symmetry 2019, 11, 1154.

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