Face Recognition based on Illumination Invariant Techniques Model

In modern years, face recognition is becoming popular in many applications in different areas being videoconferencing, security, banking, law requirement, and human-computer interaction. The performance of the face verification system depends on many challenges. The Most challenge in face recognition is illumination variations. In this approach, Median Filter, Gabor Filter, and Histogram Equalization are used to reduce the illumination effect of the face images as the preprocessing stage. The extraction of the Eigen faces features use Principal Component Analysis (PCA). After that, recognize face by using multiclass Support Vector Machines. The standard databases of ORL and Yale are used in the experiment. The results show that the system efficiently increased the accuracy of face recognition rate of the system, especially in various lighting situations.