Sunpreet S. Arora

On iris camera interoperability

With the advancements in iris matching and growing number of system deployments, a wide variety of iris cameras are now being manufactured. These cameras differ in manufacturing technology, including image acquisition spectrum and illumination settings. For large scale applications (e.g. UID system in India) where cameras from several vendors are likely to be used for iris enrollment and authentication, iris camera interoperability is an important consideration. The question we address here is: will the matching accuracy differ in matching iris images captured by two different cameras compared to images captured by the same camera? We propose an iris camera classification-based preprocessing framework to address iris interoperability. The camera classification output is used to perform selective iris image enhancement. Experimental results on the IIITD Multi-Sensor Iris database collected locally and the Notre Dame Cross Sensor database show a significant improvement in the cross-camera iris recognition accuracy using the proposed approach.

Iris recognition under alcohol influence: A preliminary study

Iris recognition has been used mainly to recognize cooperative subjects in controlled environments. With the continuing improvements in iris matching performance and reduction in the cost of iris scanners, the technology will witness broader applications and may be confronted with newer challenges. In this research, we have investigated one such challenge, namely matching iris images captured before and after alcohol consumption. Due to alcohol consumption, the pupil dilates/constricts which causes deformation in iris pattern, possibly affecting iris recognition performance. The experiments performed on the “IIITD Iris Under Alcohol Influence” database show that in matching pre and post alcohol consumption images, the overlap between genuine and impostor match score distributions increases by approximately 20%. These results on a relatively small database suggest that about one in five subjects under alcohol influence may be able to evade identification by iris recognition.

Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage

One of the major goals of most national, international and non-governmental health organizations is to eradicate the occurrence of vaccine-preventable childhood diseases (e.g., polio). Without a high vaccination coverage in a country or a geographical region, these deadly diseases take a heavy toll on children. Therefore, it is important for an effective immunization program to keep track of children who have been immunized and those who have received the required booster shots during the first 4 years of life to improve the vaccination coverage. Given that children, as well as the adults, in low income countries typically do not have any form of identification documents which can be used for this purpose, we address the following question: can fingerprints be effectively used to recognize children from birth to 4 years? We have collected 1,600 fingerprint images (500 ppi) of 20 infants and toddlers captured over a 30-day period in East Lansing, Michigan and 420 fingerprints of 70 infants and toddlers at two different health clinics in Benin, West Africa. We devised the following strategies to improve the fingerprint recognition accuracy when comparing the acquired fingerprints against an extended gallery database of 32,768 infant fingerprints collected by VaxTrac in Benin: (i) upsample the acquired fingerprint image to facilitate minutiae extraction, (ii) match the query print against templates created from each enrollment impression and fuse the match scores, (iii) fuse the match scores of the thumb and index finger, and (iv) update the gallery with fingerprints acquired over multiple sessions. A rank-1 (rank-10) identification accuracy of 83.8% (89.6%) on the East Lansing data, and 40.00% (48.57%) on the Benin data is obtained after incorporating these strategies when matching infant and toddler fingerprints using a commercial fingerprint SDK. This is an improvement of about 38% and 20%, respectively, on the two datasets without using the proposed strategies. A state-of-the-art latent finger-print SDK achieves an even higher rank-1 (rank-10) identification accuracy of 98.97% (99.39%) and 67.14% (71.43%) on the two datasets, respectively, using these strategies; an improvement of about 23% and 24%, respectively, on the two datasets without using the proposed strategies.

Fingerprint Recognition of Young Children

In 1899, Galton first captured ink-on-paper fingerprints of a single child from birth until the age of 4.5 years, manually compared the prints, and concluded that “the print of a child at the age of 2.5 years would serve to identify him ever after.” Since then, ink-on-paper fingerprinting and manual comparison methods have been superseded by digital capture and automatic fingerprint comparison techniques, but only a few feasibility studies on child fingerprint recognition have been conducted. Here, we present the first systematic and rigorous longitudinal study that addresses the following questions: 1) Do fingerprints of young children possess the salient features required to uniquely recognize a child? 2) If so, at what age can a child’s fingerprints be captured with sufficient fidelity for recognition? 3) Can a child’s fingerprints be used to reliably recognize the child as he ages? For this paper, we collected fingerprints of 309 children (0–5 years old) four different times over a one year period. We show, for the first time, that fingerprints acquired from a child as young as 6-h old exhibit distinguishing features necessary for recognition, and that state-of-the-art fingerprint technology achieves high recognition accuracy (98.9% true accept rate at 0.1% false accept rate) for children older than six months. In addition, we use mixed-effects statistical models to study the persistence of child fingerprint recognition accuracy and show that the recognition accuracy is not significantly affected over the one year time lapse in our data. Given rapidly growing requirements to recognize children for vaccination tracking, delivery of supplementary food, and national identification documents, this paper demonstrates that fingerprint recognition of young children (six months and older) is a viable solution based on available capture and recognition technology.

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.

Giving Infants an Identity: Fingerprint Sensing and Recognition

There is a growing demand for biometrics-based recognition of children for a number of applications, particularly in developing countries where children do not have any form of identification. These applications include tracking child vaccination schedules, identifying missing children, preventing fraud in food subsidies, and preventing newborn baby swaps in hospitals. Our objective is to develop a fingerprint-based identification system for infants (age range: 0-12 months)1. Our ongoing research has addressed the following issues: (i) design of a compact, comfortable, high-resolution (>1,000 ppi) fingerprint reader; (ii) image enhancement algorithms to improve quality of infant fingerprint images; and (iii) collection of longitudinal infant fingerprint data to evaluate identification accuracy over time. This collaboration between Michigan State University, Dayalbagh Educational Institute, Saran Ashram Hospital, Agra, India and NEC Corporation, has demonstrated the feasibility of recognizing infants older than 4 weeks using fingerprints.

3D Fingerprint Phantoms

One of the critical factors prior to deployment of any large scale biometric system is to have a realistic estimate of its matching performance. In practice, evaluations are conducted on the operational data to set an appropriate threshold on match scores before the actual deployment. These performance estimates, though, are restricted by the amount of available test data. To overcome this limitation, use of a large number of 2D synthetic fingerprints for evaluating fingerprint systems had been proposed. However, the utility of 2D synthetic fingerprints is limited in the context of testing end-to-end fingerprint systems which involve the entire matching process, from image acquisition to feature extraction and matching. For a comprehensive evaluation of fingerprint systems, we propose creating 3D fingerprint phantoms (phantoms or imaging phantoms are specially designed objects with known properties scanned or imaged to evaluate, analyze, and tune the performance of various imaging devices) with known characteristics (e.g., type, singular points and minutiae) by (i) projecting 2D synthetic fingerprints with known characteristics onto a generic 3D finger surface and (ii) printing the 3D fingerprint phantoms using a commodity 3D printer. Preliminary experimental results show that the captured images of the 3D fingerprint phantoms can be successfully matched to the 2D synthetic fingerprint images (from which the phantoms were generated) using a commercial fingerprint matcher. This demonstrates that our method preserves the ridges and valleys during the 3D fingerprint phantom creation process ensuring that the synthesized 3D phantoms can be utilized for comprehensive evaluations of fingerprint systems.

Design and Fabrication of 3D Fingerprint Targets

Standard targets are typically used for structural (white-box) evaluation of fingerprint readers, e.g., for calibrating imaging components of a reader. However, there is no standard method for behavioral (black-box) evaluation of fingerprint readers in operational settings where variations in finger placement by the user are encountered. The goal of this research is to design and fabricate 3D targets for repeatable behavioral evaluation of fingerprint readers. 2D calibration patterns with known characteristics (e.g., sinusoidal gratings of pre-specified orientation and frequency, and fingerprints with known singular points and minutiae) are projected onto a generic 3D finger surface to create electronic 3D targets. A state-of-the-art 3D printer (Stratasys Objet350 Connex) is used to fabricate wearable 3D targets with materials similar in hardness and elasticity to the human finger skin. The 3D printed targets are cleaned using 2M NaOH solution to obtain evaluation-ready 3D targets. Our experimental results show that: 1) features present in the 2D calibration pattern are preserved during the creation of the electronic 3D target; 2) features engraved on the electronic 3D target are preserved during the physical 3D target fabrication; and 3) intra-class variability between multiple impressions of the physical 3D target is small. We also demonstrate that the generated 3D targets are suitable for behavioral evaluation of three different (500/1000 ppi) PIV/Appendix F certified optical fingerprint readers in the operational settings.

A feedback paradigm for latent fingerprint matching

Latent fingerprints are of critical value in forensic science because they serve as an important source of evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, due to poor latent image quality in general, latent fingerprint matching accuracy is far from satisfactory. In this research, we propose a novel latent matching paradigm which takes feedback from an exemplar print during matching to refine the features extracted from the latent. The refined latent features are then used to update the baseline match scores and resort the candidate list retrieved from the database. Experimental results show that the feedback based matching mechanism improves the rank-1 identification accuracy of the baseline latent matcher by about 8% and 3% for NIST SD27 and WVU latent databases, respectively. The proposed feedback paradigm can be wrapped around any latent matcher to improve its performance.

Gold Fingers: 3D Targets for Evaluating Capacitive Readers

With capacitive fingerprint readers being increasingly used for access control as well as for smartphone unlock and payments, there is a growing interest among metrology agencies (e.g., the National Institute of Standards and Technology) to develop standard artifacts (targets) and procedures for repeatable evaluation of capacitive readers. We present our design and fabrication procedures to create conductive 3D targets (gold fingers) for capacitive readers. Wearable 3D targets with known feature markings (e.g., fingerprint ridge flow and ridge spacing) are first fabricated using a high-resolution 3D printer. A sputter coating process is subsequently used to deposit a thin layer (~300 nm) of conductive materials (titanium and gold) on 3D printed targets. The wearable gold finger targets are used to evaluate a PIV-certified single-finger capacitive reader as well as small-area capacitive readers embedded in smartphones and access control terminals. In additional, we show that a simple procedure to create 3D printed spoofs with conductive carbon coating is able to successfully spoof a PIV-certified single-finger capacitive reader as well as a capacitive reader embedded in an access control terminal.