TY  - JOUR
A1  - Erick Kurniawan
A1  - Sri Suwarno
UR  - https://doi.org/10.30812/matrik.v22i2.2551
SN  - 1858-4144
AV  - public
N2  - 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%.
SP  - 195
VL  - 22
Y1  - 2023///
TI  - MULTI-LEVEL POOLING MODEL FOR FINGERPRINT-BASED GENDER CLASSIFICATION
EP  - 206
JF  - MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer
IS  - 2
ID  - katalog9112
PB  - Universitas Bumigora, Mataram, Nusa Tenggara Barat
ER  -