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.