Fingerprint Pattern Feature Extraction for Loop Fingerprint Pattern Identification by Zhang-Suen and Stentiford Thinning Method

Abstract

Every fingerprint that a human possesses must have a fingerprint pattern and must be unique. Each pattern has a ridge pattern that will not change as long as there is no change due to an accident or injury. This ridge pattern can be used as biometric recognition. Based on the ridge pattern owned, the fingerprint pattern is divided into 4 patterns, including the tented arch pattern, the whorl pattern, the loop pattern (ulnar loop and radial loop). The stage of fingerprint pattern recognition begins by looking for minutiae (termination and bifurcation) points on the fingerprint using the Crossing Number (CN) method. Before searching for CN, the fingerprint image must be processed using the Otsu and Zhang-Suen or Stentiford Thinning methods. Thinning is used to facilitate the extraction of minutiae in fingerprint images. The last stage in obtaining the minutiae point is classified by the Linear Discriminant Analysis (LDA) method. The results obtained from the test data were 30 images, 24 images could be classified correctly, and 4 images could not be recognized. The accuracy rate of the fingerprint pattern identification system is 80%.