MULTI-LEVEL POOLING MODEL FOR FINGERPRINT-BASED GENDER CLASSIFICATION

Erick Kurniawan and Sri Suwarno (2023) MULTI-LEVEL POOLING MODEL FOR FINGERPRINT-BASED GENDER CLASSIFICATION. MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 22 (2). pp. 195-206. ISSN 1858-4144

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Abstract

It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results in classifying images. The strength of CNN lies in the type and the number of layers that construct it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and its lengthy training time. Although datasets are available, labeling that data is a significant problem. This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is removing convolution layers and directly processing fingerprint images using pooling layers. Three pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used to generate fingerprint features to classify their owner gender. These pooling layers are arranged consecutively up to eight levels. Removing convolution layers makes the process straightforward, and the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed method resulted in an accuracy of 61% to 71.5% or an average of 66.25%.

Item Type:	Article
Subjects:	T Teknologi > T Teknologi (Umum)
Divisions:	Fakultas Teknologi Informasi
Depositing User:	Admin Repository
Date Deposited:	06 Sep 2024 03:14
Last Modified:	06 Sep 2024 03:14
URI:	http://katalog.ukdw.ac.id/id/eprint/9112

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