Recognition of The Baby Footprint Characteristics Using Wavelet Method and K-Nearest Neighbor (K-NN)

  • I Made Aris Satia Widiatmika Udayana University
  • I Nyoman Piarsa Udayana University
  • Arida Ferti Syafiandini Yonsei University

Abstract

Individual recognition using biometric technology can be utilized in creating security systems that are important in modern life. The individuals recognition in hospitals generally done by conventional system so it makes more time in taking identity. A newborn baby will proceed an identity tagging after birth process is complete. This identity using a bracelet filled with names and ink stamps on paper that will be prone to damage or crime. The solution is to store the baby's identity data digitally and carry out the baby's identification process. This system can increase safety and efficiency in storing a baby's footprint image. The implementation of baby's footprint image identification starting from the acquisition of baby's footprint image, preprocessing such as selecting ROI size baby's footprint object, feature extraction using wavelet method and classification process using K-Nearest Neighbor (K-NN) method because this method has been widely used in several studies of biometric identification systems. The test data came from 30 classes with 180 images test right and left baby's footprint. The identification results using 200x500 size ROI with level 4 wavelet decomposition get recognition results with an accuracy of 99.30%, 90.17% precision, and 89.44% recall with a test computation time of 8.0370 seconds.


 

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Published
2021-03-30
How to Cite
WIDIATMIKA, I Made Aris Satia; PIARSA, I Nyoman; SYAFIANDINI, Arida Ferti. Recognition of The Baby Footprint Characteristics Using Wavelet Method and K-Nearest Neighbor (K-NN). Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 1, p. 41-52, mar. 2021. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/70638>. Date accessed: 12 may 2021. doi: https://doi.org/10.24843/LKJITI.2021.v12.i01.p05.