skip to main content

Explore Research Products in the NSF-PAR

This content will become publicly available on June 13, 2024

Title: Blink detection for off-angle iris images using deep learning
Iris recognition is one of the well-known areas of biometric research. However, in real-world scenarios, subjects may not always provide fully open eyes, which can negatively impact the performance of existing systems. Therefore, the detection of blinking eyes in iris images is crucial to ensure reliable biometric data. In this paper, we propose a deep learning-based method using a convolutional neural network to classify blinking eyes in off-angle iris images into four different categories: fully-blinked, half-blinked, half-opened, and fully-opened. The dataset used in our experiments includes 6500 images of 113 subjects and contains images of a mixture of both frontal and off-angle views of the eyes from -50 to 50 in gaze angle. We train and test our approach using both frontal and off-angle images and achieve high classification performance for both types of images. Compared to training the network with only frontal images, our approach shows significantly better performance when tested on off-angle images. These findings suggest that training the model with a more diverse set of off-angle images can improve its performance for off-angle blink detection, which is crucial for real-world applications where the iris images are often captured at different angles. Overall, the deep learning-based blink detection method can be used as a standalone algorithm or integrated into existing standoff biometrics frameworks to improve their accuracy and reliability, particularly in scenarios where subjects may blink.  more » « less
Award ID(s):
2100483
NSF-PAR ID:
10431642
Author(s) / Creator(s):
; ;
Editor(s):
Alam, Mohammad S.; Asari, Vijayan K.
Date Published:
Journal Name:
Pattern Recognition and Tracking XXXIV
Volume:
1252707
Page Range / eLocation ID:
6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ( , Multimodal Image Exploitation and Learning)
    Agaian, Sos S. ; DelMarco, Stephen P. ; Asari, Vijayan K. (Ed.)
    Iris recognition is a widely used biometric technology that has high accuracy and reliability in well-controlled environments. However, the recognition accuracy can significantly degrade in non-ideal scenarios, such as off-angle iris images. To address these challenges, deep learning frameworks have been proposed to identify subjects through their off-angle iris images. Traditional CNN-based iris recognition systems train a single deep network using multiple off-angle iris image of the same subject to extract the gaze invariant features and test incoming off-angle images with this single network to classify it into same subject class. In another approach, multiple shallow networks are trained for each gaze angle that will be the experts for specific gaze angles. When testing an off-angle iris image, we first estimate the gaze angle and feed the probe image to its corresponding network for recognition. In this paper, we present an analysis of the performance of both single and multimodal deep learning frameworks to identify subjects through their off-angle iris images. Specifically, we compare the performance of a single AlexNet with multiple SqueezeNet models. SqueezeNet is a variation of the AlexNet that uses 50x fewer parameters and is optimized for devices with limited computational resources. Multi-model approach using multiple shallow networks, where each network is an expert for a specific gaze angle. Our experiments are conducted on an off-angle iris dataset consisting of 100 subjects captured at 10-degree intervals between -50 to +50 degrees. The results indicate that angles that are more distant from the trained angles have lower model accuracy than the angles that are closer to the trained gaze angle. Our findings suggest that the use of SqueezeNet, which requires fewer parameters than AlexNet, can enable iris recognition on devices with limited computational resources while maintaining accuracy. Overall, the results of this study can contribute to the development of more robust iris recognition systems that can perform well in non-ideal scenarios. 
    more » « less
  2. ( , 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
  3. ; ; ( , IET Biometrics)
    null (Ed.)
    Accurate segmentation and parameterization of the iris in eye images still remain a significant challenge for achieving robust iris recognition, especially in off‐angle images captured in less constrained environments. While deep learning techniques (i.e. segmentation‐based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off‐angle iris recognition using such modules. In this work, the general effect of different gaze angles on ocular biometrics is discussed, and the findings are then related to the CNN‐based off‐angle iris segmentation results and the subsequent recognition performance. An improvement scheme is also introduced to compensate for some segmentation degradations caused by the off‐angle distortions, and a new gaze‐angle estimation and parameterization module is further proposed to estimate and re‐project (correct) the offangle iris images back to frontal view. Taking benefit of these, several approaches (pipelines) are formulated to configure an end‐to‐end framework for the CNN‐based offangle iris segmentation and recognition. Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off‐angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. Finally, the recognition performance of the selected approach is compared against some state‐of‐the‐art off‐angle iris recognition algorithms. 
    more » « less
  4. ; ; ( , 2020 International Conference of the Biometrics Special Interest Group (BIOSIG))
    null (Ed.)
    While deep learning techniques are increasingly becoming a tool of choice for iris segmentation, yet there is no comprehensive recognition framework dedicated for off-angle iris recognition using such modules. In this work, we investigate the effect of different gaze-angles on the CNN based off-angle iris segmentations, and their recognition performance, introducing an improvement scheme to compensate for some segmentation degradations caused by the off-angle distortions. Also, we propose an off-angle parameterization algorithm to re-project the off-angle images back to frontal view. Taking benefit of these, we further investigate if: (i) improving the segmentation outputs and/or correcting the iris images before or after the segmentation, can compensate for off-angle distortions, or (ii) the generalization capability of the network can be improved, by training it on iris images of different gaze-angles. In each experimental step, segmentation accuracy and the recognition performance are evaluated, and the results are analyzed and compared. 
    more » « less
  5. ; ( , 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
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email