In this paper, a comparative study between standard linear subspace techniques such as eigenfaces and fisherfaces and a novel morphological elastic graph matching for frontal face verification is presented. A set of experiments has been conducted in the M2VTS database in order to investigate the performance of each algorithm in different image alignment conditions. The experimental results indi...

This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central principles in neural coding early in the visual system. These principles may be relevant to how we think about higher visual processes such as face recognition as well. The paper first reviews examples of dependency lear...

Recently, a method called (PC)A was proposed to deal with face recognition with one training image per person. As an extension of the standard eigenface technique, (PC)A combines linearly each original face image with its corresponding first-order projection into a new face and then performs principal component analysis (PCA) on a set of the newly combined (training) images. It was reported tha...

Face recognition systems can normally attain good accuracy when they are provided with a large set of training examples. However, when a large training set is not available, their performance is commonly poor. In this work we describe a method for face recognition that achieves good results when only a very small training set is available (it can work with a training set as small as one image p...

Principal component analysis (PCA) is an extensively used dimensionality reduction technique, with important applications in many fields such as pattern recognition, computer vision and statistics. It employs the eigenvectors of the covariance matrix of the data to project it on a lower dimensional subspace. Kernel PCA, a generalized version of PCA, performs PCA implicitly in a nonlinearly tran...

This chapter focuses on the principles behind methods currently used for face recognition, which have a wide variety of uses from biometrics, surveillance and forensics. After a brief description of how faces can be detected in images, we describe 2D feature extraction methods that operate on all the image pixels in the face detected region: Eigenfaces and Fisherfaces first proposed in the earl...

Recently, a method called (PC)A was proposed to deal with face recognition with one training image per person. As an extension of the standard eigenface technique, (PC)A combines the original face image with its first-order projection and then performs principal component analysis (PCA) on the enriched version of the image. It was reported that (PC)A could achieve higher accuracy than the eigen...

In this paper we propose a low-complexity face verification system based on the Walsh–Hadamard transform. This system can be easily implemented on a fixed point processor and offers a good compromise between computational burden and verification rates. We have evaluated that with 36 integer coefficients per face we achieve better Detection Cost Function (6.05%) than the classical eigenfaces app...

Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. For facial images, the factors include different facial geometries, expressions, head poses, and lighting conditions. We apply multilinear algebra, the algebra of higherorder tensors, to obtain a parsimonious representation of facial image ensembles which separates these facto...

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmen...