A Novel Face Recognition System by the Combination of Multiple Feature Descriptors

Face recognition system best suits several security based applications such as access control system and identity verification system. A robust system to recognise human faces, which relies upon features, is proposed in this work. Initially, the reference face is created and the features are extracted from the reference face by feature descriptors such as Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Gabor Local Vector Pattern (GLVP). The extracted features are combined together and are clustered by employing cuckoo search algorithm. Finally in the testing phase, the face is recognised by Extreme Learning Machine (ELM), which differentiates faces by considering facial features. The public database ‘Faces 95' is exploited for analysing the performance of the system. The proposed work is analysed for its performance and evaluated against existing algorithms such as Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), combination of CCA and k Nearest Neighbour (kNN) and combination of CCA and Support Vector Machine (SVM) and experimental results are satisfactory in terms of accuracy, misclassification rate, sensitivity and specificity