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Single sample face identification

Shahali, Fatemeh, Nazemi, Azadeh and Azimifar, Zohreh (2018) Single sample face identification. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP), 25-27 Oct 2017, Shiraz, Iran.

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Abstract

This paper describes three methods to improve single sample dataset face identification. The recent approaches to address this issue use intensity and do not guarantee the high accuracy under uncontrolled conditions. This research presents an approach based on Sparse Discriminative Multi Manifold Embedding (SDMME), which uses feature extraction rather than intensity and normalization for pre-processing to reduce the effects of an uncontrolled condition such as illumination. In average this study improves identification accuracy by about 17% compared to current methods

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Face Identification; Sparse Discriminative Multi Manifold Embedding (SDMME); Single Sample dataset; Feature extraction; Self Quotient Image ( SQI)
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: 2017 Artificial Intelligence and Signal Processing Conference (AISP), Proceedings. . IEEE.
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/AISP.2017.8324123
Copyright Information:©2017 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:23501
Deposited On:01 Jul 2019 09:36 by Azadeh Nazemi . Last Modified 29 Oct 2019 13:13

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