High confidence visual recognition of persons by a test of statistical independence

Abstract

Abstruct-A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris: An estimate of its statistical complexity in a sample of the human population reveals variation corresponding to several hundred independent degrees-of-freedom. Morphogenetic randomness in the texture expressed phenotypically in the iris trabecular meshwork ensures that a test of statistical independence on two coded patterns originating from different eyes is passed almost certainly, whereas the same test is failed almost certainly when the compared codes originate from the same eye. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code." Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about lo"'.

[Invited Paper] Recent Advances in Biometric Recognition

“…Most of the commercial iris recognition systems implement the iriscode algorithm proposed by Daugman 46) , which is a famous iris recognition algorithm. The standard procedure of the iriscode algorithm is briefly described in the following.…”

“…Iris segmentation needs to find the pupillary and limbic boundaries of the iris, localize its upper and lower eyelids if they occlude, and detect and exclude any superimposed occlusions of eyelashes, shadows, or reflections. The traditional approach of iris segmentation employs circle fitting 46) , while this approach cannot be used under unconstrained conditions. To accurately segment the iris region depending on the iris shape, Shar et al used geodesic active contours (GACs) 47) and He et al used an elastic model with spline-based edge fitting 48) .…”

A New Iris Identification Method Based on Ridgelet Transform

“…Due to compensate the iris size variation, iris normalization is an obligation. As the inner and outer boundaries are approximately circle shape, we do iris normalization according to Daugman model [1] which is shown in Fig. 4 Fig.…”

“…Among existing different structural features, it was shown that exclusive features like iris pattern have very suitable features [1]. Some properties of the human iris that enhance its suitability for automatic identification include: 1) its inherent isolation and protection from the external environment, being an internal organ of the eye, behind the cornea and the aqueous humor 2) the impossibility of surgically modifying it without high risk of damaging the user's vision 3) its physiological response to light, which provides the detection of a dead or plastic iris and 4) never are there two irises alike, even for identical twin.…”

A Neuro-fuzzy Approach to User Attention Recognition

“…In eye gaze estimation systems, the eye detection accuracy plays a significant role depending on the application (higher for security, medical and control systems). Some of the existing eye gaze techniques ( [10], [11]), estimate the iris contours (circles or ellipses) on the image plane and, using edge operators, detect the iris outer boundaries. While not a lot of work has been done towards combining eye gaze and head pose information, in a non-intrusive environment, the proposed method uses a combination of the two inputs, together with other biometrics to infer the user's attention in front of a computer monitor, without the need of any special equipment apart from a simple web camera facing the user.…”