Access Control to Security Areas Based on Facial Classification

The methods of biometric access control are currently booming due to increased security checks at business and organizational areas. Belong to this area applications based on fingerprints and iris of the eye, among others. However, although there are many papers related to facial recognition, in fact it is difficult to apply to real-world applications because of variations in lighting, position and changing expressions and appearance. In addition, systems proposed in the laboratory do not usually contain a large volume of samples, or the test variations not may be used in applications in real environments. Works include the issue of recognition of the individual, but not the access control based only on facial detect, although there are applications that combine cards with facial recognition, working more on the verification that identification. This paper proposes a robust system of classification based on a multilayer neural network, whose input will be samples of facial photographs with different variations of lighting, position and even time, with a volume of samples that simulates a real environment. Output is not the recognition of the individual, but the class to which it belongs. Through the experiments, it is demonstrated that this relatively simple structure is enough to select the main characteristics of the individuals, and, in the same process, enable the network to correctly classify individuals before entering the restricted area.