Dynamic Features for Iris Recognition

Abstract

The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.

Texture code matrix-based multi-instance iris recognition

“…For CASIA-V3 database, we achieve better performance than Ma et al [1], Costa and Gonzaga [51], and Nabti and Bouridane [53]. Performances of Monro et al [52] and the proposed method are same, in fact 100 %, for identification.…”

“…Ma et al [1] used circular symmetric filter based on Gabor filters and obtained the recognition scores for 109 subjects. Costa and Gonzaga [51] used dynamic features of pupil contraction and dilation. An iris coding method based on DCT coefficients of overlapped patches from normalized iris was demonstrated by Monro et al [52] for 308 subjects.…”

Iris recognition based on distance similarity and PCA

“…The related research on classification methods that used the distance measurements can be seen in Table 1, [12,[15][16][17][18][19]: …”

“…Therefore, iris biometrics is one of the most accurate recognition methods. It is based on patterns that look like rings, grooves, spots, crowns, colors and more [15].…”

A multi-biometric iris recognition system based on a deep learning approach

“…Radon transform + gradient-based isolation Euclidean distance 4:1 (training:testing) De Costa and Gonzaga [66] Dynamic features Euclidean distance Cross-validation Dhage et al [75] DWT + DCT Euclidean distance 9:1 (training:testing) Elgamal and Al-Biqami [73] DWT + PCA KNN -Kerim et al [65] Co-occurrence matrix Euclidean distance -Li et al [70] ALBP KNN + SVM 4:1 (training:testing) Ma et al [63] Circular symmetric filter Nearest feature line 3:2 (training:testing) Minaee et al [74] Scattering transform Minimum distance Cross-validation Ng et al [67] Haar wavelet transform Hamming distance -Nalla and Chalavadi [72] Log-Gabor wavelet Online Dictionary Learning Cross-validation Roy et al [69] Multi-perturbation Shapley analysis SVM Cross-validation Umer et al [57] TCM with ordered PB SVM + Fusion Leave-one-out Vatsa et al [64] Gabor transform and euler numbers Mahalanobis distance Cross-validation Zhang and Guan [68] Empirical mode decomposition KNN -IrisConvNet system Convolutional Neural Network Softmax classifier + fusion Cross-validation…”