Machine Learning Technique and Normalization Cross Correlation Model Applied for Face Recognition
Face recognition systems just like any other biometric systems have continued to stand the test of time as a reliable means of human verification and identification. The high rate of fraud, crime, and terrorism in Nigeria and the world at large makes it increasingly necessary to have recognition systems that will be compatible with security devices currently deployed. However, the accuracy of facial recognition system is dependent on the adequacy of the model applied. This work applies a combination of Support Vector Machine (SVM) and Normalization Cross Correlation (NCC) starting with a preprocessing stage that involves filtering, cropping, normalization as well as histogram equalization of the face images. The facial images were trained and classified using Support Vector Machine then verified by NCC. The experimental study of the model with benchmarked face images showed that the model is very suitable for obtaining a better accuracy level. The False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GAR) and Total Error Rate (TER) values established the superiority of the proposed model over some related ones.
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