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1. Longitudinal study of voice recognition in children

   Purnapatra, Sandip; Das, Priyanka; Holsopple, Laura; Schuckers, Stephanie (September 2020, GIEdition)

   null (Ed.)

   Speaker recognition as a biometric modality is on the rise in the consumer marketplace for banking, online services, and personal assistant services with a potential for wider application areas. Most current applications involve adults. One of the biggest challenges in speaker recognition for children is the change in the voice properties as a child age. This work proposes a baseline longitudinal dataset from the same 30 children in the age group of 4 to 14 years over a time frame of 2.5 years and evaluates speaker recognition performance in children with the available speaker recognition technology. 

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2. Off-angle iris recognition using single and multiple deep learning frameworks

   https://doi.org/10.1117/12.2662862  

   Chavarro, David; Karakaya, Mahmut (June 2023, 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. 

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3. A Feasibility Study on Utilizing Toe Prints for Biometric Verification of Children

   https://doi.org/10.1109/ICB45273.2019.8987273  

   Yambay, David; Johnson, Morgan; Bahmani, Keivan; Schuckers, Stephanie (June 2019, 12th IAPR International Conference On Biometrics)

   Biometric recognition allows a person to be identified by comparing feature vectors derived from a person's physiological characteristics. Recognition is dependent on the permanence of the biometric characteristics over long periods of time. There was been limited work evaluating the footprint as a potential biometric. This paper presents a longitudinal study of toe prints in children to understand if this biometric modality could be used reliably as a child grows. Data was collected and analyzed in children ages 4-13 years over five visits, spaced approximately six months apart, giving two years of data. This is the first footprint collection spanning this broad age range in children. Footprints were segmented into separate toe prints to examine whether current fingerprint recognition technology can provide accurate results on toe prints. Data was analyzed using two available fingerprint matchers, Verifinger and Bo-zorth3 from NIST Biometric Image Software (NBIS). Ver-ifinger provides the best verification match scores using the toe prints, especially when using the hallux, the large toe. The hallux toe on Verifinger provides verification rates of 0% FAR and FRR for images collected on the same day and a FRR of 6.44% at a 1% FAR after two years have passed between collections. Additional longitudinal data is being collected to further these results. 

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4. Comparison of Recognition Fusion Methods for Left and Right Off-Angle Iris Images

   https://doi.org/10.1109/SoutheastCon56624.2025.10971484  

   Oz, Hasim; Aygun, Hayri; Karakaya, Mahmut (March 2025, IEEE)

   Robust iris recognition performance remains a significant challenge, especially for off-angle images captured in less constrained environments. While convolutional neural networks (CNNs) have shown great promise in iris recognition, there is limited research on the effects of gaze-angle distortions on recognition performance and the development of dedicated frameworks for off angle iris recognition. This study investigates different recognition fusion strategies for left and right off-angle iris images using deep learning. A transfer learning approach leveraging the pre-trained AlexNet model is employed to classify iris images, where frontal-view iris images are used for training and off-angle images for testing. Three fusion strategies are explored: (i) a double model approach with decision-level fusion, where separate models are trained for left and right irises and their predictions are combined, (ii) a single model approach with feature-level fusion, where a unified model extracts and fuses features from both irises, and (iii) a single model approach with image-level fusion, where left and right iris images are merged at the input level. The performance of these methods is evaluated using accuracy as the primary metric to assess the model's generalization capabilities under off-angle conditions. Experimental results highlight the advantages and trade-offs of each fusion strategy, offering insights into the role of bilateral iris information in enhancing recognition performance. The findings of this study contribute to the development of more robust deep learning-based iris recognition systems capable of handling off-angle variations. 

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5. Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking

   https://doi.org/10.1109/BTAS.2017.8272744  

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

   Iris recognition in visible spectrum has developed into an active area of research. This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications. In this paper, we present the first detailed analysis of the effect of contact lenses on iris recognition in visible spectrum. We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions. We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems. Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 82.85% is observed. This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images. 

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