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Block dictionary learning-driven convolutional neural networks for fewshot face recognition

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Abstract

Fewshot face recognition (FFR) in less constrained environment is an important but challenging task due to the lack of sufficient sample information and the impact of occlusion. In this paper, a novel approach called block dictionary learning (BDL) is proposed, which combines sparse representation with convolutional neural networks to address the FFR problem. Based on the key-point locations of face images, the images are divided into four block regions for local feature extraction. Then, highly compact and discriminative features of both holistic and segmented parts are generated by CNN, which further compensates for the shortage of samples. Moreover, the sparse loss is introduced to optimize the performance of CNN by increasing the inter-class variations of features; thus, it develops a global-to-local dictionary learning algorithm to improve the robustness of BDL against complex variations. Finally, extensive experiments on AR and Extended Yale B datasets significantly demonstrate the effectiveness of BDL in comparison with other FFR methods.

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Acknowledgements

Part of this research was carried out at Key Laboratory of Measurement and Control of Complex Systems of Engineering, Nanjing, China. Acknowledgements. This work was supported by the Natural National Science Foundation of China (Grant Nos. 51475092, 61462072) and Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20181269, BK20160693). This project was funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Fundamental Research Funds for the Central Universities.

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  1. School of Automation, Southeast University, Nanjing, China

    Qiao Du & Feipeng Da

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  1. Qiao Du
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  2. Feipeng Da
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Correspondence to Qiao Du.

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Du, Q., Da, F. Block dictionary learning-driven convolutional neural networks for fewshot face recognition. Vis Comput 37, 663–672 (2021). https://doi.org/10.1007/s00371-020-01802-y

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