Infant Footprint Recognition

Eryun Liu; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1653-1660

Infant recognition has received increasing attention in recent years in many applications, such as tracking child vaccination and identifying missing children. Due to the lack of efficient identification methods for infants and newborns, the current methods of infant recognition rely on identification of parents or certificates of identity. While biometric recognition technologies (e.g., face and fingerprint recognition) have been widely deployed in many applications for recognizing adults and teenagers, no such recognition systems yet exist for infants or newborns. One of the major problems is that the biometric traits of infants and newborns are either not permanent (e.g., face) or difficult to capture (e.g., fingerprint) due to lack of appropriate sensors. In this paper, we investigate the feasibility of infant recognition by their footprint using a 500 ppi commodity friction ridge sensor. We collected an infant footprint dataset in three sessions, consisting of 60 subjects, with age range from 1 to 9 months. We proposed a new minutia descriptor based on deep convolutional neural network for measuring minutiae similarity. The descriptor is compact and highly discriminative. We conducted verification experiments for both single enrolled template and fusion of multiple enrolled templates, and show the impact of age and time gap on matching performance. Comparison experiments with state of the art algorithm show the advantage of the proposed minutia descriptor.