Optimization of Residual Network 50 using Boosted Anisotropic Diffusion Filter and Contrast Limited Adaptive Histogram Equalization for Fingerprint Classification

Abstract

Abstract. Biometrics itself can be interpreted as a computerized method that uses aspects of biology, especially unique characteristics possessed by humans. Unique characteristics that can be used include fingerprints, geometric shapes of the hand, sound frequency keys, iris patterns, and retinas that generally differ from one individual to another. Fingerprints are the result of reproduction of the palm of the finger, either intentionally taken, stamped with ink, or marks left on objects because they have been touched by the skin of the palms of the hands or feet. Fingerprints are used as identification and verification as a means for security. So, there is a tool used to carry out this task, namely AFIS (Automatic Fingerprint Identification System). The purpose of this system is to strive for strong and fast detection. So, a fingerprint grouping or classification is needed, so that the identification process takes place faster. The algorithm used to classify fingerprints is ResNet-50. The data used came from the National Institute of Standards and Technology in 2000 (NIST-DB4 in 2000). In this dataset, there are 4000 data with each number per class is 800 data. There are five classes in this dataset including arch. right loop, left loop, tended arch, and whorl. In the training process, data processing is carried out first. This is done to optimize the accuracy produced during the training process. This research used preprocessing Boosted Anisotropic Diffusion Filter (BADF) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The BADF method is used to reduce the noise present in the image. Whereas, CLAHE is used to adjust the contrast of the image. The accuracy produced using the two preprocessing was 94.5%. Purpose: This research aims to optimize fingerprint classification using ResNet-50 combined with Boosted Anisotropic Diffusion Filter (BADF) and Contrast Limited Adaptive Equalization (CLAHE) methods. Methods: This research uses the ResNet-50 method combined with the Boosted Anisotropic Diffusion Filter (BADF) and Contrast Limited Adaptive Histogram Equalization techniques CLAHE) methods. Result: This research has four experiments, including an experiment using the ResNet-50 model without using preprocessing to obtain an accuracy of 92.5%. When BADF preprocessing was applied in the data training process, the accuracy increased to 93.5%. Meanwhile, the experiment using the ResNet-50 model using preprocessing obtained an accuracy of 94%. This accuracy can still be improved by combining BADF and CLAHE preprocessing which gets an accuracy of 94.5%. Novelty: This research uses the ResNet-50 model with a preprocessing method that is combined to obtain higher accuracy. The update in this research is to apply the BADF and CLAHE methods as image preprocessing. The BADF method aims to reduce the noise present in the scattered image, while the CLAHE method is used to adjust the contrast in the image itself.

Keywords

• Image Classification
• ResNet-50
• Contrast Limited Adaptive Equalization (CLAHE)
• Boosted Anisotropic Diffusion Filter (BADF)