Efficiency of Recognition Methods for Single Sample per Person Based Face Recognition

Abstract

Even for the present-day computer technology, the biometric recognition of human face is a difficult task and continually evolving concept in the area of biometric recognition. The area of face recognition is well-described today in many papers and books, e.g. (Delac et al., 2008), (Li & Jain, 2005), (Oravec et al., 2010). The idea that two-dimensional still-image face recognition in controlled environment is already a solved task is generally accepted and several benchmarks evaluating recognition results were done in this area (e.g. Face Recognition Vendor Tests, FRVT 2000, 2002, 2006, http://www.frvt.org/). Nevertheless, many tasks have to be solved, such as recognition in unconstrained environment, recognition of non-frontal images, single sample per person problem, etc. This chapter deals with single sample per person face recognition (also called one sample per person problem). This topic is related to small sample size problem in pattern recognition. Although there are also advantages of single sample – fast and easy creation of a face database and modest requirements for storage, face recognition methods usually fail to work if only one training sample per person is available. In this chapter, we concentrate on the following items: • Mapping the state-of-the-art of single sample face recognition approaches after year 2006 (the period till 2006 is covered by the detailed survey (Tan et al., 2006)). • Generating new face patterns in order to enlarge the database containing single samples per subject only. Such approaches can include modifications of original face samples using e.g. noise, mean filtering, suitable image transform (forward transform, then neglecting some coefficients and image reconstruction by inverse transform), or generating synthetic samples by ASM (active shape method) and AAM (active appearance method). • Comparing recognition efficiency using single and multiple samples per subject. We illustrate the influence of number of training samples per subject to recognition efficiency for selected methods. We use PCA (principal component analysis), MLP (multilayer perceptron), RBF (radial basis function) network, kernel methods and LBP (local binary patterns). We compare results using single and multiple training samples per person for images taken from FERET database. For our experiments, we selected large image set from FERET database.