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- Title
Misclassified Samples based Hierarchical Cascaded Classifier for Video Face Recognition.
- Authors
Zheyi Fan; Shuqin Weng; Yajun Zeng; Jiao Jiang; Fengqian Pang; Zhiwen Liu
- Abstract
Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Hereinwe address this problem byproposing a hierarchicalcascaded classifier forvideo face recognition, which is a multi-layer algorithm and accounts for the misclassified samplesplus their similar samples.Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.
- Subjects
FACE perception; FACIAL expression; PATHOGNOMY; CASCADED counters; NEURAL circuitry
- Publication
KSII Transactions on Internet & Information Systems, 2017, Vol 11, Issue 2, p785
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2017.02.009