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Título
A CBR system for efficient face recognition under partial occlusion.
Autor(es)
Assunto
Face recognition
Partial occlusion
Dimensionality reduction
Clasificación UNESCO
1203.04 Inteligencia Artificial
1203.17 Informática
Fecha de publicación
2017
Editor
Springer, Lecture Notes in Computer Science
Citación
López-Sánchez, D., Corchado, J. M., & Arrieta, A. G. (2017). A CBR system for efficient face recognition under partial occlusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10339 LNAI, 170-184. https://doi.org/10.1007/978-3-319-61030-6_12
Serie / N.º
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);10339 LNAI
Resumen
This work focuses on the design and validation of a CBR system for efficient face recognition under partial occlusion conditions.
The proposed CBR system is based on a classical distance-based classification method, modified to increase its robustness to partial occlusion. This is achieved by using a novel dissimilarity function which discards features coming from occluded facial regions. In addition, we explore the integration of an efficient dimensionality reduction method into the proposed framework to reduce computational cost. We present experimental results showing that the proposed CBR system outperforms classical
methods of similar computational requirements in the task of face recognition under partial occlusion.
Descrição
INTERNATIONAL CONFERENCE ON CASE-BASED REASONING (ICCBR)
The GII-GRIN-SCIE (GGS) Conference Rating Class 3; Qualified ClassesCORE:B.
URI
ISBN
9783319610290
ISSN
1611-3349, 0302-9743
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