Preprocessing Algorithim for Digital Fingerprint Image Recognition

respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .

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A fingerprint image recognition method for network user identification

Proceedings ICCI `92: Fourth International Conference on Computing and Information ◽

10.1109/icci.1992.227634 ◽

2003 ◽

Cited By ~ 5

Author(s):

T. Kobayashi

Keyword(s):

Image Recognition ◽

Recognition Method ◽

User Identification ◽

Fingerprint Image

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Design of Artificial Neural Networks to Recognize Fingerprint Patterns

IJISTECH (International Journal Of Information System & Technology) ◽

10.30645/ijistech.v3i1.34 ◽

2019 ◽

Vol 3 (1) ◽

pp. 58

Author(s):

Frinto Tambunan ◽

Yudi Y ◽

Muhammad Fauzi

Keyword(s):

Pattern Recognition ◽

Recognition System ◽

Research Data ◽

Training Process ◽

Fingerprint Image ◽

Training Time ◽

Digital Fingerprint ◽

Fingerprint Pattern ◽

Level Of Understanding ◽

Threshold Process

Image or pattern recognition system is one of the branches in computer science, this system can help the processing of fingerprint patterns, especially in the banking, police and users of other institutions who really feel the importance of using fingerprints. Several stages in fingerprint pattern image recognition are through the process of scanning, then the resulting digital fingerprint image is converted to a certain value, among others, the threshold process, the division of images, and representation of input values. The training process is carried out using two treatments: the first with a different level of understanding and the second training with different unit numbers, the best training is obtained with a level of understanding of 0.3 and the number of hidden units 10 by producing a short training time and relatively small errors. Fingerprint pattern recognition is done by two trials, based on 1 number of training patterns and 5 number of training patterns. From the research data, the ability of the system to recognize output patterns is greater if the number of training patterns increases, with a number of 1 training patterns, the system is able to recognize 50% external patterns while the 5 system training patterns are able to recognize 70% output patterns.

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Fingerprints Image Recognition by Using Perceptron Artificial Neural Network

BULETIN FISIKA ◽

10.24843/bf.2020.v21.i02.p01 ◽

2020 ◽

Vol 21 (2) ◽

pp. 37

Author(s):

Muhammad Arif Budiman ◽

I Gusti Agung Widagda

Keyword(s):

Image Recognition ◽

Good Method ◽

Pattern Identification ◽

Biometric System ◽

Security Systems ◽

Fingerprint Image ◽

Intelligence System ◽

Artificial Neural ◽

Identity Cards ◽

Verification Process

Security systems that use passwords or identity cards can be hacked and misused. One of alternative security system is to use biometric identification. The biometric system that is popularly used is fingerprints, because the system is safe and comfortable. Fingerprints have a distinctive pattern for each individual and this makes fingerprints relatively difficult to fake, so the system is safe. Comfortable because the verification process is easily done. The problem that often occurs on the system of fingerprint scanner is found an error and the user has difficulty when accessing. To handle with these problems has developed an artificial intelligence system. One of arificial intelligence in pattern identification is artificial neural networks (ANN). From some of the results of previous research showed that the ANN method is reliable in pattern identification. Based on these facts, the method used in this research is the perceptron ANN method with values learning rate varying. In the research the program conducted by testing 20 samples showed that the performance of the perceptron ANN method is relatively good method in fingerprint image recognition. This can be indicated from the value of accuracy (0.95), precision (0.83), TP rate (1), and FP rate (0.07)). In addition, the location of the point coordinate (FP rate; TP rate) is (0.07; 1) in ROC graphs is located on the upper left (perfect classifier region).

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