Local Binary Pattern and PCA Approaches: Towards for Developing Face recognition system - Mahmood Hadi, Amjad; ., Alaaabdalihadi (International Journal of Engineering and Technology, 2018)
`Face recognition is one of biometrics system used for surveillanceÂ Â purpose.Â It use toÂ Â discovery criminals, suspected terroristsÂ Â and missed children. The face recognition is term to identify human by using algorithms. In this paper,Â Â Local Binary Pattern (LBP)Â Â approachÂ Â has applied to extract features for face recognition. LBP approach is one of powerful and robust method for extraction features from face. Main contributions of this present research workÂ Â are:Â Â FirstÂ Â LBPÂ Â Â approachÂ Â used to extractÂ Â importance features from face.Â Second:Â Feature selection methods, Principal Component Analysis (PCA)Â Â has employed to remove irrelevant featuresÂ Â Â for increaseÂ Â accuracy of classification. For face recognition theÂ Â most significant featuresÂ Â Â Â Â Â are necessaryÂ Â Â due toÂ Â the face has more dimensionality. Third,Â Â Classification,Â Â Two classifiers are applied for recognition purpose.Â Support Vector Machine (SVM) and Linear Discriminates (LD) algorithmsÂ Â Â haveÂ Â usedÂ Â to classify the featuresÂ Â vector which has obtained by LBP method for face recognition. ForÂ experimentalÂ analysis ,Â theÂ Â Olivetti Research LaboratoryÂ (ORL ) standard dataÂ Â Â hasÂ beenÂ usedÂ to evaluateÂ the proposed system. The empiricalÂ Â analysis resultsÂ Â of proposed system showÂ Â that it is better in termsÂ Â of accuracy performance measures. A ComparativeÂ Â analysis results between the classifiers with all features and with PCA method is presented.Â It is observed thatÂ Â Â the performance of classifiers with whole features isÂ Â better. However, the classifier withÂ Â PCAÂ Â method is better in cost of time. Finally, it concluded that theÂ Â proposedÂ Â systemÂ Â isÂ Â more robust and betterÂ Â Â for face recognition.Â