Employing Efficient Techniques Based On 2D Dmwt/Fastica For Supervised Facial Recognition

Keywords

FastICA; MultiWavelet Transform; Supervised Face Recognition; Wavelet Transform

Abstract

A supervised facial recognition system is proposed in this paper. There are three main phases in the proposed system, namely, Preprocessing, Feature Extraction, and Classification. Cropping, choosing appropriate dimensions, and prefiltering are performed in the first phase. In the feature extraction phase, Two Dimensional Discrete Multiwavelet Transform (2D DMWT) is applied to the facial images to compact the data and extract useful information. Then, Two Dimensional Fast Independent Component Analysis (2D FastICA) is applied to the extracted features to obtain efficient features with enhanced discriminating and independent properties. Finally, the efficient features are compressed in one single column by applying ℓ2-Norm. Then, the computed ℓ2-Norm features are fed into a Neural Network (NN) classifier, which employs Back Propagation Training Algorithm (BPTA) for the recognition task. The proposed techniques are evaluated using four different databases, namely, ORL, YALE, FERET, and FEI that have different facial expressions, light conditions, rotations, etc. The results of the proposed system are analyzed by using K-fold Cross Validation (CV). The experimental results of the proposed techniques are shown to improve the recognition rates, computational complexity, as well as the storage requirements compared to the some of the existing approaches.

Publication Date

1-17-2017

Publication Title

FTC 2016 - Proceedings of Future Technologies Conference

Number of Pages

1316-1323

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/FTC.2016.7821773

Socpus ID

85013632081 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85013632081

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