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All Journal EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry JOIV : International Journal on Informatics Visualization INOVTEK Polbeng - Seri Informatika Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Riyanto Sigit, Riyanto
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health Other

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Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System Setiawan, Ari; Sigit, Riyanto; Rokhana, Rika
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1546

Abstract

These days, the application of image processing in computer vision is becoming more crucial. Some situations necessitate a solution based on computer vision and growing deep learning. One method continuously developed in deep learning is the Convolutional Neural Network, with MobileNet, EfficientNet, VGG16, and others being widely used architectures. Using the CNN architecture, the dataset consists primarily of images; the more datasets there are, the more image storage space will be required. Compression via the discrete cosine transform technique is a method to address this issue. We implement the DCT compression method in the present research to get around the system's limited storage space. Using DCT, we also compare compressed and uncompressed images. All users who had been trained with each test 5 times for a total of 150 tests were given the test. Based on testing findings, the size reduction rate for compressed and uncompressed images is measured at 25%. The case study presented is face recognition, and the training results indicate that the accuracy of compressed images using the DCT approach ranges from 91.33% to 100%. Still, the accuracy of uncompressed facial images ranges from 98.17% to 100%. In addition, the accuracy of the proposed CNN architecture has increased to 87.43%, while the accuracy of MobileNet has increased by 16.75%. The accuracy of EfficientNetB1 with noisy-student weights is measured at 74.91%, and the accuracy of EfficientNetB1 with imageNet weights can reach 100%. Facial biometric authentication using a deep learning algorithm and DCT-compressed images was successfully accomplished with an accuracy value of 95.33% and an error value of 4.67%.
Co-Authors ., Justiawan Achmad Basuki Ali Ridho Barakbah Anwar Ari Setiawan Arna Fariza Arya Brahmanta, Arya Aulia Dwi Maharani Bima Sena Bayu Dewantara Fadhillah, Excel Daris Fakhri, Haidar Huda, Achmad Torikul iwan Syarif Jovan Josafat Jaenputra Kemalasari Madyono, Madyono Makarim, Ahmad Fauzi Maulana, Firja Hanif Maulana, Rifqi Affan Mochamad Mobed Bachtiar, Mochamad Mobed Nabilah, Anisah Pamenang, Muhammad Unggul Perdanasari, Lukie Rahagiyanto, Angga Ratna Kusumawati Rika Rokhana Rochmad, Moch. Rokhana, Rika Romadhon, Nur Rizky Ryando, Catoer Sanusi, Muhammad Anwar Sena Bayu Dewantara, Bima Setiawardhana Setiawardhana, Setiawardhana Taufiq Hidayat Tita Karlita Wibowo, Galih Hendra Yuniarti, Heny Zainal Arief Zamzami, Amran