Periocular Recognition by Detection of Local Symmetry Patterns

Abstract

Abstract-We present a new system for biometric recognition using periocular images. The feature extraction method employed describes neighborhoods around keypoints by projection onto harmonic functions which estimates the presence of a series of various symmetric curve families around such keypoints. The iso-curves of such functions are highly symmetric w.r.t. the keypoints and the estimated coefficients have well defined geometric interpretations. The descriptors used are referred to as Symmetry Assessment by Feature Expansion (SAFE). Extraction is done across a set of discrete points of the image, uniformly distributed in a rectangular-shaped grid positioned in the eye center. Experiments are done with two databases of iris data, one acquired with a close-up iris camera, and another in visible light with a webcam. The two databases have been annotated manually, meaning that the radius and center of the pupil and sclera circles are available, which are used as input for the experiments. Results show that this new system has a performance comparable with other periocular recognition approaches. We particularly carry out comparative experiments with another periocular system based on Gabor features extracted from the same set of grid points, with the fusion of the two systems resulting in an improved performance. We also evaluate an iris texture matcher, providing fusion results with the periocular systems as well.

Cross-eyed 2017: Cross-spectral iris/periocular recognition competition

“…The HH system exploits the fusion of algorithms based on Symmetry Patterns (SAFE) [19], Gabor Spectral Decomposition (GABOR) [3], SIFT [5], Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG) [23]. The scores of individual systems are then mapped to a log-likelihood ratio according to the probabilistic Bayesian framework [4].…”

A survey on periocular biometrics research

“…Feature extraction is then done by computing a gradient orientation histogram in the neighborhood of each keypoint, in a similar way than SIFT descriptor, below. SAFE: Symmetry Assessment by Feature Expansion (Mikaelyan et al, 2014; describes neighborhoods around key points by projection onto harmonic functions which estimates the presence of various symmetric curve families. The iso-curves of such functions are highly symmetric w.r.t.…”

“…This is remarkable given the adverse acquisition conditions and the small resolution of the VW databases used. They further extended the study with their SAFE matcher (Mikaelyan et al, 2014), and a SIFT matcher.…”

Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

“…For each database, this covers approximately the range of pupil radius of all its images ( Figure 5). For the SAFE matcher, based on [10], the range of radii of the filters is 10 to 64 with VW and 5 to 60 with NIR databases. We report verification results of the periocular and iris matchers in Table 2.…”