PERSON IDENTIFICATION USING FUNCTIONAL NEAR- INFRARED SPECTROSCOPY SIGNALS USING A FULLY CONNECTED DEEP NEURAL NETWORK

Yıl 2017, Cilt: 59 Sayı: 2, 55 - 68, 21.12.2017

Ozge Mercanoglu Sıncan Hacer Yalım Keles Yagmur Kır Adnan Kusman Bora Baskak

Öz

In this study, we investigate the suitability of
functional near-infrared spectroscopy signals (fNIRS) for person identification
using data visualization and machine learning algorithms. We first applied two
linear dimension reduction algorithms: Principle Component Analysis (PCA) and
Singular Value Decomposition (SVD) in order to reduce the dimensionality of the
fNIRS data. We then inspected the clustering of samples in a 2d space using a
nonlinear projection algorithm. We observed with the SVD projection that the
data integrity associated with each person is high in the reduced space. In the
light of these observations, we implemented a random forest algorithm as a
baseline model and a fully connected deep neural network (FCDNN) as the primary
model to identify person from their brain signals. We obtained %85.16 accuracy
with our FCDNN model using SVD reduction. Our results are in parallel with the
neuroscience researches, which state that brain signals of each person are
unique and can be used to identify a person.

Anahtar Kelimeler

functional near-infrared spectroscopy (fNIRS), person identification, PCA, SVD, fully connected deep neural network, random forest

Kaynakça

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