This paper presents an approach of face recognition system using Backpropagation learning neural network algorithm introducing appearance and shape based facial features to enhance the efficiency with different lighting variations. To extract the appearance and shape based facial feature, Active Appearance Model (AMM) has been applied. Appearance based facial feature is useful when the lighting condition is uniform. On the other hand when the environmental lighting condition is different, shape based facial features can perform better in comparison with appearance based feature because shape based structure is not changed with lighting variations. In this work, both appearance and shape based facial features are combined to enhance the recognition efficiency for various light variant system. For dimensionality reduction of appearance and shape based facial features, Principal Component Analysis (PCA) method has been used. Finally, error Backpropagation learning feed forward neural network algorithm has been used to classify the facial features. To measure the performance of the proposed appearance and shape based facial recognition system, VALID database has been used where each face has been captured with four different lighting variations. Experiments have been performed with Appearance-Only, Shape-Only and combined Appearance-Shape based feature and performance of the proposed system shows the superiority of the face recognition system.
| Published in | Science Journal of Circuits, Systems and Signal Processing (Volume 2, Issue 4) |
| DOI | 10.11648/j.cssp.20130204.11 |
| Page(s) | 93-99 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
Appearance and Shape Based Facial Features, Face Recognition with Different Lighting Variations, Principal Component Analysis, Backpropagation Learning Neural Network, Active Appearance Model
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APA Style
Md. Rabiul Islam, Rizoan Toufiq, Md. Abdus Sobhan. (2013). Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations. Science Journal of Circuits, Systems and Signal Processing, 2(4), 93-99. https://doi.org/10.11648/j.cssp.20130204.11
ACS Style
Md. Rabiul Islam; Rizoan Toufiq; Md. Abdus Sobhan. Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations. Sci. J. Circuits Syst. Signal Process. 2013, 2(4), 93-99. doi: 10.11648/j.cssp.20130204.11
AMA Style
Md. Rabiul Islam, Rizoan Toufiq, Md. Abdus Sobhan. Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations. Sci J Circuits Syst Signal Process. 2013;2(4):93-99. doi: 10.11648/j.cssp.20130204.11
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Download@article{10.11648/j.cssp.20130204.11,
author = {Md. Rabiul Islam and Rizoan Toufiq and Md. Abdus Sobhan},
title = {Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations},
journal = {Science Journal of Circuits, Systems and Signal Processing},
volume = {2},
number = {4},
pages = {93-99},
doi = {10.11648/j.cssp.20130204.11},
url = {https://doi.org/10.11648/j.cssp.20130204.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20130204.11},
abstract = {This paper presents an approach of face recognition system using Backpropagation learning neural network algorithm introducing appearance and shape based facial features to enhance the efficiency with different lighting variations. To extract the appearance and shape based facial feature, Active Appearance Model (AMM) has been applied. Appearance based facial feature is useful when the lighting condition is uniform. On the other hand when the environmental lighting condition is different, shape based facial features can perform better in comparison with appearance based feature because shape based structure is not changed with lighting variations. In this work, both appearance and shape based facial features are combined to enhance the recognition efficiency for various light variant system. For dimensionality reduction of appearance and shape based facial features, Principal Component Analysis (PCA) method has been used. Finally, error Backpropagation learning feed forward neural network algorithm has been used to classify the facial features. To measure the performance of the proposed appearance and shape based facial recognition system, VALID database has been used where each face has been captured with four different lighting variations. Experiments have been performed with Appearance-Only, Shape-Only and combined Appearance-Shape based feature and performance of the proposed system shows the superiority of the face recognition system.},
year = {2013}
}
TY - JOUR T1 - Appearance and Shape Based Face Recognition Using Backpropagation Learning Neural Network Algorithm with Different Lighting Variations AU - Md. Rabiul Islam AU - Rizoan Toufiq AU - Md. Abdus Sobhan Y1 - 2013/08/30 PY - 2013 N1 - https://doi.org/10.11648/j.cssp.20130204.11 DO - 10.11648/j.cssp.20130204.11 T2 - Science Journal of Circuits, Systems and Signal Processing JF - Science Journal of Circuits, Systems and Signal Processing JO - Science Journal of Circuits, Systems and Signal Processing SP - 93 EP - 99 PB - Science Publishing Group SN - 2326-9073 UR - https://doi.org/10.11648/j.cssp.20130204.11 AB - This paper presents an approach of face recognition system using Backpropagation learning neural network algorithm introducing appearance and shape based facial features to enhance the efficiency with different lighting variations. To extract the appearance and shape based facial feature, Active Appearance Model (AMM) has been applied. Appearance based facial feature is useful when the lighting condition is uniform. On the other hand when the environmental lighting condition is different, shape based facial features can perform better in comparison with appearance based feature because shape based structure is not changed with lighting variations. In this work, both appearance and shape based facial features are combined to enhance the recognition efficiency for various light variant system. For dimensionality reduction of appearance and shape based facial features, Principal Component Analysis (PCA) method has been used. Finally, error Backpropagation learning feed forward neural network algorithm has been used to classify the facial features. To measure the performance of the proposed appearance and shape based facial recognition system, VALID database has been used where each face has been captured with four different lighting variations. Experiments have been performed with Appearance-Only, Shape-Only and combined Appearance-Shape based feature and performance of the proposed system shows the superiority of the face recognition system. VL - 2 IS - 4 ER -
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