Towards a Self-Sufficient Face Verification System

Use este enlace para citar
http://hdl.handle.net/2183/30090Colecciones
- Investigación (FIC) [1615]
Metadatos
Mostrar el registro completo del ítemTítulo
Towards a Self-Sufficient Face Verification SystemAutor(es)
Fecha
2021Cita bibliográfica
LOPEZ-LOPEZ, Eric, REGUEIRO, Carlos V., PARDO, Xosé M., FRANCO, Annalisa and LUMINI, Alessandra, 2021. Towards a self-sufficient face verification system. Expert Systems with Applications. 15 July 2021. Vol. 174, p. 114734. DOI 10.1016/j.eswa.2021.114734
Resumen
[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.
Palabras clave
Adaptive biometrics
Video surveillance
Video-to-video face verification
Unsupervised learning
Incremental learning
Video surveillance
Video-to-video face verification
Unsupervised learning
Incremental learning
Descripción
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
Versión del editor
Derechos
Atribución 4.0 Internacional