Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study

Face recognition is the process of identifying a person by their facial characteristics from a digital image or a video frame. Face recognition has extensive applications and there will be a massive development in future technologies. The main contribution of this research is to perform a comparative study between different statistical-based face recognition techniques, namely: Eigen-faces, Fisher-faces, and Local Binary Patterns Histograms (LBPH) to measure their effectiveness and efficiency using real-database images. These recognizers still used on top of commercial face recognition products. Additionally, this research is comprehensively comparing 17 face-recognition techniques adopted in research and industry that use artificial-neural network, criticize and categories them into an understandable category. Also, this research provides some directions and suggestions to overcome the direct and indirect issues for face recognition. It has found that there is no existing recognition method that the community of face recognition has agreed on and solves all the issues that face the recognition, such as different pose variation, illumination, blurry and low-resolution images. This study is important to the recognition communities, software companies, and government security officials. It has a direct impact on drawing clear path for new face recognition propositions. This study is one of the studies with respect to the size of its reviewed approaches and techniques.