A Comparison of Face Recognition Algorithms for Varying Capturing Conditions
In the recent years, great success has been achieved in the field of recognizing faces but when it comes to unconstrained environment, face recognition is still quite a demanding problem. This is due to some external factors that act as an obstruction in this process. These factors include variation in expression, pose and illumination, motion images, misalignment, etc. This research addresses different efficiently working algorithms in unconstrained scenarios. A comparison between Principal Component Analysis (PCA) and 2-Dimensional PCA (2DPCA) is performed in this paper in the end. The performance of these systems is also checked by modifying the lighting condition of the test images. The performance and robustness of these systems was evaluated and tested through experiments.