Face recognition is an importance capability of human beings. Identifying a person by face is one of the most fundamental human functions since time memorial. Automatic face recognition increases the security standards at public places and border checkpoints. The picture inside the identification documents could widely differ from the face that is scanned under random lighting conditions and for unknown poses. The paper describes an optimal combination of three key algorithms of object recognition that are able to perform in real time. The camera scan is processed by a recurrent neural network, by an Eigenfaces (PCA) method and by a least squares matching algorithm. Several examples demonstrate the achieved robustness and high recognition rate. Neural Network is a circuit designed to replicate the way neurons act and interact in the brain. Neural networks are computer systems that have the ability to recognize objects and understand speech. Neural Networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements (neurons) working in parallel to solve a specific problem, whereas Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem.
