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Dergiler / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2017 / Cilt: 59 - Sayı: 2

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

Ozge MERCANOGLU SINCAN
Hacer YALIM KELES
Yagmur KIR
Adnan KUSMAN
Bora BASKAK
Sayfa
55–68
Yayın Tarihi
2017-12-21
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri:We then inspected the clustering of samples in a 2d space using a nonlinear projection algorithm.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Anahtar Kelimeler functional near-infrared spectroscopy (fNIRS) · person identification · PCA

Özet

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.

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Bu Makaleye Atıf Yap

SINCAN, O. M., KELES, H. Y., KIR, Y., KUSMAN, A., BASKAK, B. (2017). PERSON IDENTIFICATION USING FUNCTIONAL NEAR- INFRARED SPECTROSCOPY SIGNALS USING A FULLY CONNECTED DEEP NEURAL NETWORK. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Cilt: 59 - Sayı: 2, 55–68. https://ajindex.com/articles/51173

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Kaynakça (11)

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