Biometric authenticator algorithm based on multiresolution analysis

Kerrache Soumia, Beladgham Mohammed, Hamza Aymen, Kadri Ibrahim

Abstract


In this paper, we propose a feature extraction method for two-dimensional imageauthentication algorithm using curvelet transform and principal component analysis(PCA). Since wavelet transform can not adequately describe facial curves features,Theproposed approach involves image denoising applying a 2D-Curvelet transform toachieve compact representations of curves singularities. To assess the performanceof the presented method, we have employed three classifification techniques: Neuralnetworks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).Extensive experimental results and comparison with the existing methods show the effectiveness of the proposed recognition method in the ORL face database and CASIAiris database.

Keywords


Biometric authentication; Classifification algorithmes; Curvelet transform; Feature extraction; Multiresolution analysis

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v20.i3.pp1332-1341

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics