Sub-pattern based multi-manifold discriminant analysis for face recognition

Jiangyan Dai; Changlu Guo; Wei Zhou; Yanjiao Shi; Lin Cong; Yugen Yi

doi:10.1117/12.2302491

10 April 2018 Sub-pattern based multi-manifold discriminant analysis for face recognition

Jiangyan Dai , Changlu Guo, Wei Zhou, Yanjiao Shi , Lin Cong, Yugen Yi

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150O (2018) https://doi.org/10.1117/12.2302491
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Citation Download Citation

Jiangyan Dai , Changlu Guo, Wei Zhou, Yanjiao Shi , Lin Cong, and Yugen Yi "Sub-pattern based multi-manifold discriminant analysis for face recognition", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150O (10 April 2018); https://doi.org/10.1117/12.2302491

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KEYWORDS

Facial recognition systems

Databases

Detection and tracking algorithms

Principal component analysis

Autoregressive models

Feature extraction

Information science

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