Conference Paper

Newborn's Biometric Identification: Can it be done?

Conference: VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications, Funchal, Madeira, Portugal, January 22-25, 2008 - Volume 1
Source: DBLP


In this article we propose a novel biometric identification method for newborn babies using their palmpnnts. A new high resolution optical sensor was developed, which obtains images with enough ridge minutiae to uniquely identify the baby. The palm and footprint images of 106 newborns were analysed, leading to the conclusion that palmprints yield more detailed images then footprints. Fingerprint experts from the Identification Institute of Paraná State performed two matching tests, resulting in a correct identification rate of 63.3% and 67.7%, more than three times higher than that obtained on similar experiments described on literature. The proposed image acquisition method also opens the perspective for the creation of an automatic identification system for newborns.

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Available from: Luciano Silva, Dec 23, 2013
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    • "Unfortunately, they draw a conclusion that newborn offline footprinting cannot be used for identification purposes, and then the acquisition of footprints in hospitals should be abandoned because it only generates unnecessary work and costs [5]. Moreover, the offline footprinting method is only traditional which is done after every birth just as a simple procedure without much importance over the quality of the images obtained. "

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    • "Problems related to other biometric traits are discussed by Weingaertner et al. in [27], where the authors wonder if biometric authentication of newborns is feasible at all. For instance, it is worth mentioning a quite popular technique also recommended in 1999 by Federal Bureau of Investigation (FBI), which entails to take foot and finger printing of the child and mother. "
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    ABSTRACT: Many research studies demonstrated that recognition based on ear biometrics offers an accuracy which is comparable to face trait, especially in controlled settings. Our proposal is to exploit it to avoid the problem of newborn swap, which is possible and actually happens, most of all in crowded maternity wards of big hospitals. We tested the viability of this solution using a dataset of ear images of newborns, and the obtained results testify that it is possible to decrease the probability of an error using this technique.
    2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, Rome, Italy; 10/2014
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    • "As may also be observed, the resolution plays a major role in the recognition task. For the subset NB ID II B, even using dry images which are not as good as the images in subset NB ID B, we achieved about 94% TAR with 0% FAR, while we obtained only 86% TAR with 0% FAR for the subset NB ID B. These results corroborate the analysis presented by Weingaertner et al. [25], which concludes that at least 1500 ppi are necessary to perform newborn recognition with palmprints and footprints. V. CONCLUSION We have presented a novel method for newborn authentication by matching keypoints in a hierarchical framework. "
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    ABSTRACT: We present a novel method for newborn authentication that matches keypoints in different interdigital regions from palmprints or footprints. Then, the method hierarchically combines the scores for authentication. We also present a novel pore detector for keypoint extraction, named Dynamic Pore Filtering (DPF), that does not rely on expensive processing techniques and adapts itself to different sizes and shapes of pores. We evaluated our pore detector using four different datasets. The obtained results of the DPF when using newborn dermatoglyphic patterns (2400ppi) are comparable to the state-of-the-art results for adult fingerprint images with 1000ppi. For authentication, we used four datasets acquired by two different sensors, achieving true acceptance rates of 91.53% and 93.72% for palmprints and footprints, respectively, with a false acceptance rate of 0%. We also compared our results to our previous approach on newborn identification, and we considerably outperformed its results, increasing the true acceptance rate from 71% to 98%.
    to appear at XXII International Conference on Pattern Recognition (ICPR); 08/2014
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