Ensemble of texture descriptors and classifiers for face recognition

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• 作者：Alessandra Lumini^a ; ^{alessandra.lumini@unibo.it} ; Loris Nanni^b ; ^{loris.nanni@unipd.it} ; Sheryl Brahnam^c ; ^{sbrahnam@missouristate.edu}
• 关键词：Face recognition ; Similarity metric learning ; Ensemble of descriptors ; Support vector machine
• 刊名：Applied Computing and Informatics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：13
• 期：1
• 页码：79-91
• 全文大小：860 K
• 卷排序：13

文摘

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.