Shahali, Fatemeh, Nazemi, Azadeh 
ORCID: 0000-0002-1138-309X and Azimifar, Zohreh
(2018)
Single sample face identification utilizing sparse discriminative multi manifold embedding.
In: Artificial Intelligence and Signal Processing Conference (AISP2017), 25-27 Oct 2017, Shiraz, Iran.
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 for 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
uncontrolled condition such as illumination. In average this
study improves identification accuracy about 17% compare to
current methods
Metadata
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Event Type: | Conference |
| Refereed: | No |
| 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 of the. . IEEE. |
| Publisher: | IEEE |
| Official URL: | https://doi.org/10.1109/AISP.2017.8324123 |
| Copyright Information: | © 2017 The Authors |
| ID Code: | 23057 |
| Deposited On: | 04 Mar 2019 10:24 by Azadeh Nazemi . Last Modified 03 Sep 2020 15:58 |
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