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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. Analysis of Dilation in Children and its Impact on Iris Recognition

   https://doi.org/10.1109/IJCB48548.2020.9304911  

   Das, Priyanka; Holsopple, Laura; Schuckers, Stephanie; Schuckers, Michael (September 2020, 2020 IEEE International Joint Conference on Biometrics (IJCB))

   null (Ed.)

   The dilation of the pupil and it’s variation between a mated pair of irides has been found to be an important factor in the performance of iris recognition systems. Studies on adult irides indicated significant impact of dilation on iris recognition performance at different ages. However, the results of adults may not necessarily translate to children. This study analyzes dilation as a factor of age and over time in children, from data collected from same 209 subjects in the age group of four to 11 years at enrollment, longitudinally over three years spaced by six months. The performance of iris recognition is also analyzed in presence of dilation variation. 

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3. Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review

   https://doi.org/10.3390/jcp2030036  

   Chowdhury, A M; Imtiaz, Masudul Haider (September 2022, Journal of Cybersecurity and Privacy)

   Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications. 

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4. Self-supervised learning of video representations from a child’s perspective

   Orhan, A Emin; Wang, Wentao; Wang, Alex N; Ren, Mengye; Lake, Brenden M (August 2024, CogSci)

   Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases. 

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5. Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures

   https://doi.org/10.1109/ICB45273.2019.8987297  

   Plesh, Richard; Bahmani, Keivan; Jang, Ganghee; Yambay, David; Brownlee, Ken; Swyka, Timothy; Johnson, Peter; Ross, Arun; Schuckers, Stephanie (June 2019, 12th IAPR International Conference On Biometrics)

   Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with time-series and color-sensing capabilities to improve the robust-ness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable. 

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