Abstract
Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)2PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.
Highlights
Due to growing requirements of non-invasive recognition systems, Face Recognition has recently become a very popular area of research
Six popular subspace face recognition algorithms were tested accompanied with four popular distance metrics
An important and novel contribution of this study is that it introduced an unbiased comparative analysis of popular subspace algorithms under equal and testing working conditions, such as same pre-processing steps, same testing criteria, same testing and training sets and introduced the favorable performance conditions for each of these algorithms
Summary
Due to growing requirements of non-invasive recognition systems, Face Recognition has recently become a very popular area of research. A recent comprehensive study [1], categorizes and lists the popular face recognition algorithms and databases. This study has categorized face recognition algorithms into five categories namely linear and non-linear projection methods, neural network based methods (another non-linear solution), Gabor filter and wavelets based methods, fractal based methods and lastly thermal and hyperspectral methods. [2], in their study grouped the approaches of face recognition into two broad categories, namely appearance based and feature based. Appearance based face recognition algorithms, on the other hand, despite being dependent on primitive pixel values are still considered to be a better choice [2]. Among the appearance based methods, the so called subspace methods which rely on the dimensionality reduction of face space while preserving the most relevant information are the most famous
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