On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier

Chafia Ferhaoui-Cherifi; Mohamed Deriche

doi:10.1109/ICETAS.2017.8277850

On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier

Chafia Ferhaoui-Cherifi^*
, Mohamed Deriche

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we investigate the effects of different wavelet families as well as the effects of number of neurons on a the performance of a neural network based face recognition system. The face images are transformed using multi-level wavelets from which features are extracted. The resulting feature vectors are project over an orthogonal space using a simple PCA (Principal Component Analysis) projection. The uncorrelated transformed feature vectors are then used with an Multilayer Perceptron (MLP) based classifier. Different scenarios in terms of wavelet families and network structures are investigated. Extensive experimental results were performed using the ORL database. We show that certain families together with certain MLP structures give the best results in terms of recognition accuracy.

Original language	English
Title of host publication	4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538621066
DOIs	https://doi.org/10.1109/ICETAS.2017.8277850
State	Published - 2 Jul 2017

Publication series

Name	4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
Volume	2018-January

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Face Recognition
Feature Extraction
Multilayer Perceptron Neural Network
Wavelet Transform

ASJC Scopus subject areas

Education
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Electrical and Electronic Engineering
Mechanical Engineering

Access to Document

10.1109/ICETAS.2017.8277850

Cite this

Ferhaoui-Cherifi, C., & Deriche, M. (2017). On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier. In 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017 (pp. 1-6). (4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICETAS.2017.8277850

Ferhaoui-Cherifi, Chafia ; Deriche, Mohamed. / On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier. 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017).

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abstract = "In this paper, we investigate the effects of different wavelet families as well as the effects of number of neurons on a the performance of a neural network based face recognition system. The face images are transformed using multi-level wavelets from which features are extracted. The resulting feature vectors are project over an orthogonal space using a simple PCA (Principal Component Analysis) projection. The uncorrelated transformed feature vectors are then used with an Multilayer Perceptron (MLP) based classifier. Different scenarios in terms of wavelet families and network structures are investigated. Extensive experimental results were performed using the ORL database. We show that certain families together with certain MLP structures give the best results in terms of recognition accuracy.",

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Ferhaoui-Cherifi, C & Deriche, M 2017, On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier. in 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017. 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6. https://doi.org/10.1109/ICETAS.2017.8277850

On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier. / Ferhaoui-Cherifi, Chafia; Deriche, Mohamed.
4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Ferhaoui-Cherifi C, Deriche M. On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier. In 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017). doi: 10.1109/ICETAS.2017.8277850

On the performance of wavelet families in face recognition using a multilayer perceptron neural network classifier

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this