More Like this

1. Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization

   https://doi.org/10.1117/12.2588898  

   Ehrlich, Emily; Karakaya, Mahmut (April 2021, Disruptive Technologies in Information Sciences V)

   Blowers, Misty; Hall, Russell D.; Dasari, Venkateswara R. (Ed.)

   Iris recognition is one of the most accurate biometric recognition techniques, however off-angle iris recognition has yet to have an established comprehensive recognition framework. This is due to the difficulties in the recognition of off-angle iris image inconsistencies within the iris patterns when gaze deviations are present. In this work, we investigate different iris normalization techniques and compare their performance. The two methods under investigation include elliptical normalization and circular normalization after frontal projection of off-angle iris recognition. Elliptical normalization samples the iris texture using elliptical segmentation parameters: 𝑥, 𝑦, 𝑟1 , 𝑟2 , θ where 𝑥, 𝑦 are coordinates, 𝑟1, 𝑟2 are the radius, and θ is the orientation. Also, when investigating circular unwrapping, we will be using the ellipse segmentation parameters to estimate the gaze deviation. The image will be projected back to a frontal view using perspective transformation. Then, we segment the transformed image and normalize using the circular parameters: 𝑥, 𝑦, 𝑟 where 𝑥, 𝑦 are coordinates and r is the radius. We further investigate if: (i) elliptical normalization or circular unwrapping recognition performance is higher, and (ii) if the two segmentation methods in circular unwrapping increase the recognition efficiency 

   more » « less
2. Gender and ethnicity classification of Iris images using deep class-encoder

   https://doi.org/10.1109/BTAS.2017.8272755  

   Singh, Maneet; Nagpal, Shruti; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Majumdar, Angshul (October 2017, International Joint Conference on Biometrics (IJCB))

   Soft biometric modalities have shown their utility in different applications including reducing the search space significantly. This leads to improved recognition performance, reduced computation time, and faster processing of test samples. Some common soft biometric modalities are ethnicity, gender, age, hair color, iris color, presence of facial hair or moles, and markers. This research focuses on performing ethnicity and gender classification on iris images. We present a novel supervised autoencoder based approach, Deep Class-Encoder, which uses class labels to learn discriminative representation for the given sample by mapping the learned feature vector to its label. The proposed model is evaluated on two datasets each for ethnicity and gender classification. The results obtained using the proposed Deep Class-Encoder demonstrate its effectiveness in comparison to existing approaches and state-of-the-art methods. 

   more » « less
3. Iris Print Attack Detection using Eye Movement Signals

   https://doi.org/10.1145/3517031.3532521  

   Raju, Mehedi Hasan; Lohr, Dillon J; Komogortsev, Oleg (January 2022, 2022 Symposium on Eye Tracking Research and Applications)

   Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infra-red light and an image sensor. This similarity potentially enables eye tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art. 

   more » « less
4. Fragile Bits in Off-angle Iris Recognition

   https://doi.org/10.1109/IWBF50991.2021.9465098  

   Karakaya, Mahmut (May 2021, 2021 IEEE International Workshop on Biometrics and Forensics)

   null (Ed.)

   As an emerging biometric research, standoff iris recognition systems focus on recognition of non-cooperative subjects in much less constrained environments where their captured images are likely to be non-ideal including being off-angle. Iris biometrics convert unwrapped iris textures into binary iris codes to compare them with other saved codes by measuring their Hamming Distances. The similarity calculation assumes an equal contribution of each individual pixel in iris codes. However, previous studies showed that some pixels (aka. fragile bits) are more error prone than others even in frontal iris images. In addition, off-angle iris images are affected by several challenging factors including corneal refraction and limbus occlusion. These challenges in off-angle images also increase the fragility of bits in iris codes. This paper first presents the pixel inconsistency in iris codes of off-angle images using elliptical segmentation and normalization. The pixel fragility is a result of iris codes warping due to the refraction of light in cornea and occlusion of iris texture at limbus. As another contribution, we propose to identify these fragile pixels in iris codes using edge detection and eliminating them in Hamming distance calculation by masking these fragile bits. Based on the results, the proposed method improves the recognition performance in off-angle iris images where the average genuine Hamming distance score reduced from 0.3082 to 0.1244 and the equal error rate is lowered 19%. 

   more » « less
5. Synthetic iris presentation attack using iDCGAN

   https://doi.org/10.1109/BTAS.2017.8272756  

   Kohli, Naman; Yadav, Daksha; Vatsa, Mayank; Singh, Richa; Noore, Afzel (October 2017, International Joint Conference on Biometrics (IJCB))

   Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has raised apprehensions about susceptibility of these systems to various attacks. In the past, researchers have examined the impact of various iris presentation attacks such as textured contact lenses and print attacks. In this research, we present a novel presentation attack using deep learning based synthetic iris generation. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, we propose a new framework, named as iDCGAN (iris deep convolutional generative adversarial network) for generating realistic appearing synthetic iris images. We demonstrate the effect of these synthetically generated iris images as presentation attack on iris recognition by using a commercial system. The state-of-the-art presentation attack detection framework, DESIST is utilized to analyze if it can discriminate these synthetically generated iris images from real images. The experimental results illustrate that mitigating the proposed synthetic presentation attack is of paramount importance. 

   more » « less