Matching highly non-ideal ocular images: An information fusion approach

Abstract

We consider the problem of matching highly non-ideal ocular images where the iris information cannot be reliably used. Such images are characterized by non-uniform illumination, motion and de-focus blur, off-axis gaze, and non-linear deformations. To handle these variations, a single feature extraction and matching scheme is not sufficient. Therefore, we propose an information fusion framework where three distinct feature extraction and matching schemes are utilized in order to handle the significant variability in the input ocular images. The Gradient Orientation Histogram (GOH) scheme extracts the global information in the image; the modified Scale Invariant Feature Transform (SIFT) extracts local edge anomalies in the image; and a Probabilistic Deformation Model (PDM) handles nonlinear deformations observed in image pairs. The simple sum rule is used to combine the match scores generated by the three schemes. Experiments on the extremely challenging Face and Ocular Challenge Series (FOCS) database and a subset of the Face Recognition Grand Challenge (FRGC) database confirm the efficacy of the proposed approach to perform ocular recognition.

Mitigating effects of plastic surgery: Fusing face and ocular biometrics

“…Recent research has shown that the ocular information can be used as a soft biometric [10,8,7]. It has been experimentally demonstrated that the ocular information can be used in lieu, or to improve the matching accuracy, of the iris [12] and face [10] under non-ideal conditions. While there are no specific guidelines for the dimensions of the periocular region, Park et al [10] suggest that including the eyebrows can result in higher matching accuracy.…”

“…The combination of SIFT and LBP techniques allows for image feature extraction at both local and global levels, respectively. Furthermore, SIFT and LBP have been the most significantly used techniques 7 in the ocular recognition literature [10,12]. The use of these techniques helps in maintaining uniformity for performance comparisons.…”

Best regions for periocular recognition with NIR and visible images

“…But for visible data, blood vessels and skin were reported more helpful than eye shape and eyelashes. A similar study was done in [6], but with automatic algorithms (Probabilistic Deformation Models (PDM) [7] and m-SIFT [8]). This is, to the best of our knowledge, the only work evaluating the contribution of periocular regions to the performance of machine algorithms.…”

Person identification at a distance via ocular biometrics

“…Park et al [14] proposed periocular biometrics which focuses on discriminating features near the eye (ocular) region. Thereafter, several approaches were proposed to extend the state-of-art in ocular biometrics [3], [5], [6], [8], [10], [16], [18], [19], [24], [25], [26]. 1 http://www.sri.com/engage/products-solutions/iris-move-biometricidentification-systems a b c Figure 1: Iris images captured in unconstrained environment: (a) when periocular region is partially occluded, (b) when iris region is occluded due to closed eye lids, and (c) when both iris and periocular regions are blurred.…”