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Journal : Jurnal Teknik Informatika (JUTIF)

Multimodal Biometric Recognition Based on Fusion of Electrocardiogram and Fingerprint Using CNN, LSTM, CNN-LSTM, and DNN Models Agustina, Winda; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Saragih, Triando Hamonangan; Farmadi, Andi; Budiman, Irwan; Parenreng, Jumadi Mabe; Alkaff, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5098

Abstract

Biometric authentication offers a promising solution for enhancing the security of digital systems by leveraging individuals' unique physiological characteristics. This study proposes a multimodal authentication system using deep learning approaches to integrate fingerprint images and electrocardiogram (ECG) signals. The datasets employed include FVC2004 for fingerprint data and ECG-ID for ECG signals. Four deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and Deep Neural Network (DNN)—are evaluated to compare their effectiveness in recognizing individual identity based on fused multimodal features. Feature extraction techniques include grayscale conversion, binarization, edge detection, minutiae extraction for fingerprint images, and R-peak–based segmentation for ECG signals. The extracted features are combined using a feature-level fusion strategy to form a unified representation. Experimental results indicate that the CNN model achieves the highest classification accuracy at 96.25%, followed by LSTM and DNN at 93.75%, while CNN-LSTM performs the lowest at 11.25%. Minutiae-based features consistently yield superior results across different models, highlighting the importance of local feature descriptors in fingerprint-based identification tasks. This research advances biometric authentication by demonstrating the effectiveness of feature-level fusion and CNN architecture for accurate and robust identity recognition. The proposed system shows strong potential for secure and adaptive biometric authentication in modern digital applications.
Co-Authors Abdullayev, Vugar Agus Dwi Susanto Ahmad Zainul Abidin Ainiyyah, Ainiyyah Akhmad Rojali Aldy Heriwardito Alfando, Muhammad Alvin Andi Eriadi Andi Farmadi Andreyan Rizky Baskara ARIF RAHMAN, MUHAMMAD Arina Ihda Rahmah Syarifah Ariska Deffy Anggarany, Ariska Deffy Baskara, Andreyan Budhi Antariksa Ceva W. Pitoyo Dany Primanita Kartikasari Darmawan, Puja Dewi Rizqia Najipah Dewi Yennita Sari Dodon Turianto Nugrahadi Erlina Burhan Fajar Zulkarnain, Andry Fatma Indriani Friska Abadi Gusti Nizar Syafi'i Halimah Halimah Hayatun Nufus Henning Titi Ciptaningtyas Hera Afidjati Herry Purnomo Husnul Khatimi Ibrahim Nur Insan Putra Darmawan Iftihatul Aulia Rahmah Iphan Fitrian Radam Iphan Fitrian Radam Iqbal Rizqi, Muhammad Irwan Budiman Jumadi Mabe Parenreng Marimin Marimin Maulani, Irham Maulidiya, Erika Maya Amalia Mohamad Fahmi Alatas Muhammad Afrizal Miqdad Muhammad Fachrurrazi Muhammad Nur Abdi Muhammad Reza Faisal, Muhammad Reza Muhammad Ridho A.G.D. Muhammad Ziki Elfirman Muliadi Muti'a Maulida Mutia Maulida Nandang Eko Yulianto Nurul Fathanah Mustamin Nurul Qamaria Paramita, Diana Putra, Andika Chandra Putri Ridha Amalia Raisa Amalia Rakhmadhany Primananda, Rakhmadhany Rani Sauriasari, Rani Reza Karimi Rita Rogayah Rudy Ansari, Rudy Rudy Herteno Ryan Ramel Samoedro, Erlang Saragih, Triando Hamonangan Sa’diah, Halimatus siti sheilawati Soehardiman, Dicky Sugiantoro Sugiantoro Sugiantoro Sugiantoro Sukamto Koesnoe Sukardi Sukardi Supeno Djanali Syarifah Soraya Takhwifa, Famila Taufik, Feni Fitriani Wenny Puspita Wijaya, Eka Setya Winarto Chandra Winda Agustina Windarsyah Windarsyah Yandra Arkeman Yulianto, Nandang Eko Yuslena Sari, Yuslena