Neural gen feature selection for supervised learning classifier

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Face recognition has recently received significant attention, especially during the past few years. Many face recognition techniques were developed such as PSO-SVM and LDA-SVM However, inefficient features in the face recognition may lead to inadequate in the recognition results. Hence, a new face recognition system based on Genetic Algorithm and FFBNN technique is proposed. Our proposed face recognition system initially performs the feature extraction and these optimal features are promoted to the recognition process. In the feature extraction, the optimal features are extracted from the face image database by Genetic Algorithm (GA) with FFBNN and the computed optimal features are given to the FFBNN technique to carry out the training and testing process. The optimal features from the feature database are fed to the FFBNN for accomplishing the training process. The well trained FFBNN with the optimal features provide the recognition result. The optimal features in FFBNN by GA efficiently perform the face recognition process. The human face dataset called YALE is utilized to analyze the performance of our proposed GA-FFNN technique and also this GA-FFBNN is compared with standard SVM and PSO-SVM techniques.

Original languageEnglish
Pages (from-to)3181-3187
Number of pages7
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume7
Issue number15
Publication statusPublished - 2014

Fingerprint

Supervised learning
Face recognition
Feature extraction
Classifiers
Genetic algorithms
Particle swarm optimization (PSO)
Testing

Keywords

  • Face recognition
  • FFBNN
  • GA
  • PCA
  • SVM

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

Cite this

Neural gen feature selection for supervised learning classifier. / Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Ali Othman, Zulaiha.

In: Research Journal of Applied Sciences, Engineering and Technology, Vol. 7, No. 15, 2014, p. 3181-3187.

Research output: Contribution to journalArticle

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