C. Puri

Bio: C. Puri is an academic researcher from Indraprastha Institute of Information Technology. The author has contributed to research in topics: Fingerprint recognition & Fingerprint (computing). The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

On analysis of rural and urban indian fingerprint images

Abstract: This paper presents a feasibility study to compare the performance of fingerprint recognition on rural and urban Indian population. The analysis shows that rural population is very challenging and existing algorithms/systems are unable to provide acceptable performance. On the other hand, fingerprint recognition algorithms provide comparatively better performance on urban population. The study also shows that poor images quality, worn and damaged patterns and some special characteristics affect the performance of fingerprint recognition.

From identity to identification: Fixating the fragmented self

Abstract: This article was accepted for publication in the journal Media, Culture and Society: http://mcs.sagepub.com/content/35/1/44

The application of a biometric identification technique for linking community and hospital data in rural Ghana

Abstract: Background : The reliability of counts for estimating population dynamics and disease burdens in communities depends on the availability of a common unique identifier for matching general population data with health facility data. Biometric data has been explored as a feasible common identifier between the health data and sociocultural data of resident members in rural communities within the Kintampo Health and Demographic Surveillance System located in the central part of Ghana. Objective : Our goal was to assess the feasibility of using fingerprint identification to link community data and hospital data in a rural African setting. Design : A combination of biometrics and other personal identification techniques were used to identify individual’s resident within a surveillance population seeking care in two district hospitals. Visits from resident individuals were successfully recorded and categorized by the success of the techniques applied during identification. The successes of visits that involved identification by fingerprint were further examined by age. Results : A total of 27,662 hospital visits were linked to resident individuals. Over 85% of those visits were successfully identified using at least one identification method. Over 65% were successfully identified and linked using their fingerprints. Supervisory support from the hospital administration was critical in integrating this identification system into its routine activities. No concerns were expressed by community members about the fingerprint registration and identification processes. Conclusions : Fingerprint identification should be combined with other methods to be feasible in identifying community members in African rural settings. This can be enhanced in communities with some basic Demographic Surveillance System or census information. Keywords: biometrics; fingerprint; identification; techniques; electronic; database; data-linkage (Published: 17 March 2016) Citation: Glob Health Action 2016, 9 : 29854 - http://dx.doi.org/10.3402/gha.v9.29854

Reframing biometric surveillance: from a means of inspection to a form of control

Abstract: This paper reviews the social scientific literature on biometric surveillance, with particular attention to its potential harms. It maps the harms caused by biometric surveillance, traces their theoretical origins, and brings these harms together in one integrative framework to elucidate their cumulative power. Demonstrating these harms with examples from the United States, the European Union, and Israel, I propose that biometric surveillance be addressed, evaluated and reframed as a new form of control rather than simply another means of inspection. I conclude by delineating three features of biometric technologies—complexity, objectivity, and agency—that demonstrate their social power and draw attention to the importance of studying biometric surveillance.

Fingerprint Image Quality: Predicting Biometric Performance

Abstract: Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. We provide comprehensive algorithm descriptions and make available implementations of adaptations of 10 quality assessment algorithms from the literature which operate at the local or global image level. We evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. Our evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset. 4.

Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints

Abstract: The effectiveness of fingerprint-based authentication systems on good quality fingerprints is established long back. However, the performance of standard fingerprint matching systems on noisy and poor quality fingerprints is far from satisfactory. Towards this, we propose a data uncertainty-based framework which enables the state-of-the-art fingerprint pre-processing models to quantify noise present in the input image and identify fingerprint regions with background noise and poor ridge clarity. Quantification of noise helps the model two folds: firstly, it makes the objective function adaptive to the noise in a particular input fingerprint and consequently, helps to achieve robust performance on noisy and distorted fingerprint regions. Secondly, it provides a noise variance map which indicates noisy pixels in the input fingerprint image. The predicted noise variance map enables the end-users to understand erroneous predictions due to noise present in the input image. Extensive experimental evaluation on 13 publicly available fingerprint databases, across different architectural choices and two fingerprint processing tasks demonstrate effectiveness of the proposed framework.