Biometric face recognition: from classical statistics to future challenges

Abstract

Face recognition involves at least three major concepts from statistics: dimension reduction, feature extraction, and prediction. A selective review of algorithms, from seminal to state-of-the-art, explores how these concepts persist as organizing principles in the field. Algorithms based directly upon classical statistical techniques include linear methods like principal component analysis and linear discriminant analysis. Nonlinear manifold methods, such as Laplacianfaces and Stiefel quotients, offer considerable performance improvements. Other noteworthy ideas include three-dimensional morphable models, methods using local regions and/or alternative feature spaces (e.g., elastic bunch graph matching and local binary patterns) and sparse representation approaches. Opportunities for innovative statistical and collaborative research in face recognition are expanding in tandem with the growing complexity and diversity of applications.

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

“…Face verification performs 1:1 matching and provides a binary decision to the claimed identity. Face verification in controlled scenarios has reached a rather high accuracy (Givens et al, 2013). To tackle face verification in uncontrolled scenarios, many approaches have been proposed for more effective alignment (Cao et al, 2014;Yi et al, 2013;Chen et al, 2012), utilization of different types of feature representations (Lowe, 2004;Dalal and Triggs, 2005;Ahonen et al, 2004) and matching metric for comparing faces (Hua and Akbarzadeh, 2009;Pinto et al, 2009;Li et al, 2013).…”

“…This is because the amplitude varies slowly with spatial shift, making it robust to texture variations caused by dynamic expressions and imprecise alignment. By constructing LBP-type features mostly from the amplitude and applying various learning techniques, many Gabor based approaches have shown remarkable advantages over pixel-featured based methods: the identification rate in benchmark evaluations has been found to be improved by more than 20% (reaching around 90%) thanks to the "blessing of dimensionality" (Givens et al, 2013) ( but at the high cost of computational efficiency (Mu et al, 2011;Chai et al, 2014)). Now the question is how to achieve face identification rates in the range from 90% to 95% or even higher.…”

Bayesian face recognition using 2D Gaussian-Hermite moments

“…Traditional holistic face recognition algorithms are developed using the classical statistical techniques such as the principal component analysis (PCA), linear discriminant analysis (LDA), and canonical correlation analysis (CCA) [12]. For instance, the popular Eigenfaces method uses the PCA to reduce the dimensionality by identifying a small number of directions that capture the majority of variations in the images [13].…”

“…For instance, the popular Eigenfaces method uses the PCA to reduce the dimensionality by identifying a small number of directions that capture the majority of variations in the images [13]. The Fisherfaces method [14], which is related to the well-known multivariate analysis of variance (ANOVA) framework in statistics [12], reduces the dimensionality by using a linear projection of the images that maximizes the ratio between the interclass image variation to the intraclass variation. Methods have also been developed using the CCA approach, which allows two projection spaces-one for the training images and the other for the test images, commonly referred to as the probe images.…”

“…Methods have also been developed using the CCA approach, which allows two projection spaces-one for the training images and the other for the test images, commonly referred to as the probe images. These methods use a suitable set of canonical variables obtained from the covariance matrices of the training and probe image pairs in the two spaces to identify the subject [12]. Since the inception of the Eigenfaces and Fisherfaces methods, numerous variations and extensions of these methods that include the Eigenface maximum likelihood [15], adaptively weighted subpattern PCA [16], kernel PCA [17], null-space LDA [18], dual-space LDA [19], regularized discriminant analysis [20], boosting LDA [21], Fourier-LDA [22], Gabor-LDA [23], and incremental LDA [24] have been suggested for face recognition.…”