Towards a self-sufficient face verification system
| Autor: | Xosé M. Pardo, Eric Lopez-Lopez, Carlos V. Regueiro, Alessandra Lumini, Annalisa Franco |
|---|---|
| Přispěvatelé: | Lopez-Lopez E., Regueiro C.V., Pardo X.M., Franco A., Lumini A. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Video surveillance 02 engineering and technology Machine learning computer.software_genre Unsupervised learning Adaptive biometrics 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) Video-to-video face verification 0202 electrical engineering electronic engineering information engineering Adaptation (computer science) Incremental learning Authentication business.industry Adaptive biometric General Engineering Knowledge acquisition Computer Science Applications Support vector machine Face (geometry) Identity (object-oriented programming) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname |
| Popis: | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness. This work has received financial support from the Spanish government (project TIN2017-90135-R MINECO (FEDER)), from The Consellaría de Cultura, Educación e Ordenación Universitaria (accreditations 2016–2019, EDG431G/01 and ED431G/08), and reference competitive groups (2017–2020, and ED431C 2017/04), and from the European Regional Development Fund (ERDF). Eric López-López has received financial support from the Xunta de Galicia and the European Union (European Social Fund – ESF) Xunta de Galicia; EDG431G/01 Xunta de Galicia; ED431G/08 Xunta de Galicia; ED431C 2017/04 |
| Databáze: | OpenAIRE |
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