Face recognition based on polar frequency features

Abstract

A novel biologically motivated face recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Next, the Euclidean distance between all images is computed and each image is now represented by its dissimilarity to the other images. A Pseudo-Fisher Linear Discriminant was built on this dissimilarity space. The performance of Discrete Fourier transform (DFT) descriptors, and a combination of both feature types was also evaluated. The algorithms were tested on a 40-and 1196-subjects face database (ORL and FERET, respectively). With 5 images per subject in the training and test datasets, error rate on the ORL database was 3.8, 1.25 and 0.2% for the FBT, DFT, and the combined classifier, respectively, as compared to 2.6% achieved by the best previous algorithm. The most informative polar frequency features were concentrated at low-to-medium angular frequencies coupled to low radial frequencies. On the FERET database, where an affine normalization preprocessing was applied, the FBT algorithm outperformed only the PCA in a rank recognition test. However, it achieved performance comparable to state-of-the-art methods when evaluated by verification tests. These results indicate the high informative value of the polar frequency content of face images in relation to recognition and verification tasks, and that the Cartesian frequency content can complement information about the subjects' identity, but possibly only when the images are not pre-normalized. Possible implications for human face recognition are discussed.

A novel polar-based human face recognition computational model

“…The main motivation for this study was to improve the predictability value and increase the biologically inspired content of high-level visual tasks such as human object recognition models (7). The main contributions of this study were a) demonstrating for the first time that human visual face processing could involve the selective use of polar frequency components (8), and b) reporting direct empirical support for a recently proposed computational face recognition model (6).…”

“…These studies and the resulting theoretical models did not take into account physiological and psychophysical evidence that suggests the existence of mechanisms for visual analysis in polar coordinates (4,5). In order to fill this gap, a computationally successful biologically inspired approach to face recognition using polar domain representation has been recently reported (6). In the current study, we investigated the possibility that spatial polar-defined components are selectively used in human face processing.…”

Human and Machine Recognition of Fourier-Bessel Filtered Face Images

“…Inspired by these latter studies, we first determined the contrast sensitivity functions to fundamental patterns defined in polar Cartesian [18] and later developed an automatic face recognition system based on polar frequency features, as extracted by Fourier-Bessel transformation (FBT), and dissimilarly representation [19,21]. This system was thoroughly tested on large datasets and achieved state of the art performance when compared to previous algorithms.…”

“…In order to compare the human performance with that of a FBT-based model [19], we built a simple nearest neighbor classifier. The classifier output quantifies the similarity between the test and training objects.…”

“…These studies and the resulting theoretical models did not take into account physiological and psychophysical evidence that suggests the existence of mechanisms for visual analysis in polar coordinates (see Section 2). In order to fill this gap, a computationally successful biologically-inspired approach to face recognition using polar domain representation has been recently reported [19].…”