Human Visual System applied to Facial Recognition

Abstract

Research in the field of biometrics is constantly expanding. Numerous studies have been carried out to develop different techniques with the aim of ensuring reliable and efficient recognition systems. Of our five senses, vision takes up the most of the neurons in our brain. This makes the visual approach challenging. This paper proposes to develop a new facial recognition technique based on the concept of Human Visual System in order to simulate or imitate human perception. Our approach exploits the behavior of the Human Visual System in biometric systems to improve individual recognition. It focuses on Perceptual Channel Decomposition in order to generate images, confined around a certain frequency range, perfectly uncorrelated. For the extraction of characteristic vectors, our technique uses the Principal Component Analysis. The principle of the visual approach consists of exploiting one or more characteristics of the peripheral parts of the Human Visual System. These characteristics can integrate the sensitivity of the Human Visual System to spatial frequencies, its sensitivity to local contrast. The Principal Component Analysis uses a set of seventeen channels, each adjusted to a band of given radial frequencies and orientation. The seventeen output images contain the same spatial information but are perfectly uncorrelated from a spectral point of view. In the implementation phase, we limited ourselves to explore this technique by using only four frequency rings. The results obtained are conclusive and satisfactory.