Features Fusion based on the Fisherface and Simplification of the Laplacian Smoothing Transform

Abstract

The newest model was proposed to extract the characteristic as distinctive attribute based on the Simplification of the Laplacian Smoothing Transform (S-LST) and the Fisherface. The proposed model is composed of two primary processes, i.e., training and testing processes. A training process involved features extraction based on the S-LST and the Fisherface, selection of the principal features, and features fusion of them. The proposed model has proved that the features can preserve the particular global information and the local feature areas of the rows and columns. The testing process is the stage to obtain the primary features through the new projection of the training sets. The last operation of the testing process is to measure the similarity of the features. The proposed model was tested using three different databases, i.e., the ORL, the YALE, and the UoB. The proposed model demonstrated the best performance, which is 100% recognition rate on the ORL and YALE when five images are implemented to the training sets. The maximum average of the recognition rate is 98.7% for the ORL database, 97.7% for the YALE database, and 94.53% for the UoB database. The comparison result showed that the proposed model performs better than the other approach, i.e., LST+LDA, S-MFA, S-NPE, S-LPP, S-LDA, LPP, LDA, and PCA.

Deep Learning Architecture Based on Convolutional Neural Network (Cnn) in Image Classification

“…There were several algorithms used in image classification before the popular CNN method. The image features are then entered into a classification algorithm such as SVM (Support vector machines), in this algorithm uses image pixel level values as input feature vectors [4] [5].…”