Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View

Abstract

Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

“…This complex procedure entails systematically examining and comparing fingerprint patterns to identify prospective connections between criminal incident information and current evidence (Tang et al 2017). This evaluation, provided by advanced technology such as automatic fingerprint identification systems (AFISs), plays an important role in criminal investigations, providing substantial information for resolving difficult crimes (Adam et al 2021). This has transformed the way in which government intelligence organizations approach criminal identification, improving the reliability and effectiveness of current investigation techniques.…”

A Novel Fingerprint Liveness Detection Method using Empirical Mode Decomposition and Neural Network

“…These important lines play different roles. One of them is to introduce frictions between finger and objects, by using this friction we can grab, write or touch objects [2]. Fingerprint is the oldest method of recognition and the progress in technology has increased its variety.…”

Finger Veins Verification by Exploiting the Deep Learning Technique

“…Finger biometrics are among the oldest types of biometrics used for identification. One of its types has been used and is still used in paper and electronic applications, which is the fingerprint [1,2]. In addition to many other finger biometrics such as the finger inner knuckle [3,4], finger outer knuckles [5,6], Finger Geometry [7,8], Finger Texture (FT) [9][10][11], and the finger vein (FV) [12,13] which is discussed in this work.…”

“…It has advantages, including (1) It cannot be imitated or stolen because it is under the skin. (2) The body must be alive. (3) It has unique patterns for each person, even among twins.…”