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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Dyah Aruming Tyas
Universitas Gadjah Mada
Computer Science & IT Engineering

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Contact Lens Detection Using Domain Specific BSIF and Discrete Wavelet Transform Muhamad Ilham Aji Vachroni; Raden Sumiharto; Dyah Aruming Tyas
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.20084

Abstract

Iris is one of the reliable biometrics because it has a texture that rich properties and the texture is not changeable lifetime. Iris recognition has drawbacks in the matching process when using contact lenses. Contact lens can changes in the texture of the iris, which can reduce the accuracy of recognition. Therefore, a system is needed to detect contact lenses while someone is detected using contact lens, the system can reject the registration or authentication process. Methods used to detect contact lenses are Domain Specific Binarized Statistical Image Feature (BSIF) and Discrete Wavelet Transform (DWT) for feature extraction. Both methods are fused and modeled using the Support Vector Machine (SVM). Based on the test results, the most optimal kernel is 5x5 12bit. Using the kernel, the accuracy and f1 score obtained 99.1%. In the experiments conducted, this research applies Principal Component Analysis (PCA) to reduce features. However, the role of PCA does not affect the performance of the model. The best model tested with real life data, the Pocophone f1 smartphone and CCTV were used to take pictures of the eyes. The Result 6 experiments wich are 4 without contact lenses and 2 wearing contact lenses, there are only 2 detected correctly. This is because the ability of the images taken from the Poco F1 and CCTV have low resolution.
IMPLEMENTASI MASK-RCNN PADA DATASET KECIL CITRA SEL DARAH MERAH BERDASARKAN KRITERIA WARNA SEL Dyah Aruming Tyas; Tri Ratnaningsih
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 1 (2023): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i1.3026

Abstract

Examination of red blood cell morphology is one of the diagnostic aids for several diseases, one of which is anemia. The development of the application of digital image processing technology, artificial intelligence, and computer-assisted diagnosis opens opportunities to solve various problems related to medical images. Red blood cells sticking together or overlapping is a challenge in the red blood cell segmentation process which ultimately affects the results of cell type identification. A method that can perform instance segmentation is needed to overcome this problem. This study aims to implement the Mask-RCNN algorithm on a small red blood cell image dataset and evaluate the prediction results' performance. Based on the research results, the attached red blood cells can be detected individually by the model, and the accuracy of the cell detection results is 68.27%. Mask-RCNN can be used for blood cell segmentation instances and blood cell detection on small datasets, but the model accuracy still needs to be improved. Therefore it is necessary to do further research by increasing the number of datasets used.
Co-Authors Muhamad Ilham Aji Vachroni Raden Sumiharto Tri Ratnaningsih