Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW) dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357.

Alessandra, L., Loris, N., Sheryl, B. (2017). Ensemble of texture descriptors and classifiers for face recognition. APPLIED COMPUTING AND INFORMATICS, 13(1), 79-91 [10.1016/j.aci.2016.04.001].

Ensemble of texture descriptors and classifiers for face recognition

Alessandra Lumini
Writing – Original Draft Preparation
;
2017

Abstract

Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW) dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357.
2017
Alessandra, L., Loris, N., Sheryl, B. (2017). Ensemble of texture descriptors and classifiers for face recognition. APPLIED COMPUTING AND INFORMATICS, 13(1), 79-91 [10.1016/j.aci.2016.04.001].
Alessandra, Lumini; Loris, Nanni; Sheryl, Brahnam
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/616618
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