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.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1016/j.aci.2016.04.001
Rights Information
© 2016 The authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
face recognition, similarity metric learning, ensemble of descriptors, support vector machine
Publication Date
2017
Recommended Citation
Lumini, Alessandra, Loris Nanni, and Sheryl Brahnam. "Ensemble of texture descriptors and classifiers for face recognition." Applied Computing and Informatics 13, no. 1 (2017): 79-91.
Journal Title
Applied Computing and Informatics
