Evaluation of Performance of Kernel-Based Feature Extraction Techniques for Face Recognition System

Proceedings of the International conference on Applied Research in Engineering,Science and Technology

Year: 2018 | Page No:111-120

DOI: http://www.doi.org/10.33422/icarest.2018.09.45

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Evaluation of Performance of Kernel-Based Feature Extraction Techniques for Face Recognition System

Fenwa Olusayo D., Ajala Funmilola A. and Makinde Bukola O.

 

ABSTRACT: 

Face recognition is considered to be one of the most reliable biometrics where security issues are of concerned. Feature extraction which is a functional block of a face recognition system becomes a critical problem when there is need to obtain the best feature with minimum classification error and low running time. Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these systems remain an open problem. Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component Analysis) for face recognition system. A database of 360 face images was created by obtaining facial images from LAUTECH Biometric Research Group consisting of six facial expressions of 60 persons. Images were preprocessed (gray scaling, cropping and histogram equalization) and the kernel variants were used to extract distinctive features and reduce the dimensionality of each of the images from 600×800 pixels to four smaller dimensions: 50×50, 100×100, 150×150 and 200×200 pixels. Euclidean Distance similarity measure was used for classification. The performance of the three kernel variants was evaluated for face recognition system using 180 images for training and 180 images for testing using Recognition Accuracy (RA) and Recognition Time (RT). Empirical results indicate that KLDA performs best for face recognition system with an average accuracy of 94.52%. The larger image dimension also results in better recognition performance. We intend to experiment on other classifiers for face recognition system in our future work

Keywords:Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear.