Unconstrained Long Range Iris Recognition

Long range iris acquisition system for stationary and mobile subjects

(S. Venugopalan, U. Prasad, K. Harun, K. Neblett, D. Toomey, J. Heyman and M. Savvides, "Long range iris acquisition system for stationary and mobile subjects," in International Joint Conference on Biometrics (IJCB), pp.1,8, 11-13 Oct. 2011)


Most iris based biometric systems require a lot of co- operation from the users so that iris images of acceptable quality may be acquired. Features from these may then be used for recognition purposes. Relatively fewer works in literature address the question of less cooperative iris acquisition systems in order to reduce constraints on users. In this paper, we describe our ongoing work in designing and developing such a system. It is capable of capturing images of the iris up to distances of 8 meters with a resolution of 200 pixels across the diameter. If the resolution requirement is decreased to 150 pixels, then the same system may be used to capture images from up to 12 meters. We have incorporated velocity estimation and focus tracking modules so that images may be acquired from subjects on the move as well. We describe the various components that make up the system, including the lenses used, the imaging sensor, our auto-focus function and velocity estimation module. All the hardware components are Commercial Off The Shelf (COTS) with little or no modifications. We also present preliminary iris acquisition results using our system for both stationary and mobile subjects.


Unconstrained Iris Acquisition and Recognition Using COTS PTZ Camera

(S. Venugopalan and M. Savvides "Unconstrained Iris Acquisition and Recognition using COTS PTZ camera," in EURASIP Journal on Advances in Signal Processing, Vol. 2010, No. 38, February 2010)


Uniqueness of iris patterns among individuals has resulted in the ubiquity of iris recognition systems in virtual and physical spaces, at high security facilities around the globe. Traditional methods of acquiring iris patterns in commercial systems scan the iris when an individual is at a predetermined location in front of the scanner. Most state-of-the-art techniques for unconstrained iris acquisition in literature use expensive custom equipment and are composed of a multicamera setup, which is bulky, expensive, and requires calibration. This paper investigates a method of unconstrained iris acquisition and recognition using a single commercial off-the-shelf (COTS) pan-tilt-zoom (PTZ) camera, that is compact and that reduces the cost of the final system, compared to other proposed hierarchical multicomponent systems. We employ state-of-the-art techniques for face detection and a robust eye detection scheme using active shape models for accurate landmark localization. Additionally, our system alleviates the need for any calibration stage prior to its use. We present results using a database of iris images captured using our system, while operating in an unconstrained acquisition mode at 1.5 m standoff, yielding an iris diameter in the 150–200 pixels range.


An Automatic Iris Occlusion Estimation Method based on High Dimensional Density Estimation

(Y. Li and M. Savvides, "An Automatic Iris Occlusion Estimation Method based on High Dimensional Density Estimation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 4, pp. 784-796, April 2013)


Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

A Bayesian Approach to Deformed Pattern Matching of Iris Images

(J. Thornton, M. Savvides and V. Kumar, "A Bayesian Approach to Deformed Pattern Matching of Iris Images," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, pp. 595-606, April 2007)


In this article, we describe a general probabilistic framework for matching patterns that experience in-plane nonlinear deformations, such as iris patterns. Given a pair of images, we derive a maximum a posteriori probability (MAP) estimate of the parameters of the relative deformation between them. Our estimation process accomplishes two things simultaneously: it normalizes for pattern warping and it returns a distortion-tolerant similarity metric which can be used for matching two nonlinearly deformed image patterns. The prior probability of the deformation parameters is specific to the pattern-type and, therefore, should result in more accurate matching than an arbitrary general distribution. We show that the proposed method is very well suited for handling iris biometrics, applying it to two databases of iris images which contain real instances of warped patterns. We demonstrate a significant improvement in matching accuracy using the proposed deformed Bayesian matching methodology. We also show that the additional computation required to estimate the deformation is relatively inexpensive, making it suitable for real-time applications.





© Carnegie Mellon University Biometrics Center 2015

  • s-twitter
  • s-facebook