Intelligent Local Face Recognition

Abstract

Our faces are complex objects with features that can vary over time. However, we humans have a natural ability to recognize faces and identify persons in a glance. Of course, our natural recognition ability extends beyond face recognition, where we are equally able to quickly recognize patterns, sounds or smells. Unfortunately, this natural ability does not exist in machines, thus the need to simulate recognition artificially in our attempts to create intelligent autonomous machines. Intelligent systems are being increasingly developed aiming to simulate our perception of various inputs (patterns) such as images, sounds...etc. Biometrics, in general, and facial recognition in particular are examples of popular applications for artificial intelligent systems. Face recognition by machines can be invaluable and has various important applications in real life, such as, electronic and physical access control, national defence and international security. Simulating our face recognition natural ability in machines is a difficult task, but not impossible. Throughout our life time, many faces are seen and stored naturally in our memories forming a kind of database. Machine recognition of faces requires also a database which is usually built using facial images, where sometimes different face images of a one person are included to account for variations in facial features. The development of an intelligent face recognition system requires providing sufficient information and meaningful data during machine learning of a face. This chapter presents a brief review of known face recognition methods such as Principal Component Analysis (PCA) (Turk & Pentland, 1991), Linear Discriminant Analysis (LDA) (Belhumeur et al., 1997) and Locality Preserving Projections (LPP) (He et al., 2005), in addition to intelligent face recognition systems that use neural networks such as (Khashman, 2006) and (Khashman, 2007). There are many works emerging every year suggesting different methods for face recognition (Delac & Grgic, 2007); these methods are mostly appearance-based or feature-based methods that search for certain global or local representation of a face. The chapter will also provide a detailed case study on intelligent local face recognition, where a neural network is used to identify a person upon presenting his/her face image. Local pattern averaging is used for face image preprocessing prior to training or testing the neural network. Averaging is a simple but efficient method that creates fuzzy patterns as compared to multiple crisp patterns, which provides the neural network with meaningful learning while reducing computational expense. In previous work (Khashman, 2007) an intelligent global face recognition system which considers a person’s face and its background was presented, and suggestions were made O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg