Deep Learning Architecture Model for Iris Image Segmentation in Biometrics

Arief Andy Soebroto, Wayan Firdaus Mahmudy, Nurul Hidayat, Rekyan Regasari Mardi Putri, Anto Satriyo Nugroho

Abstract


Abstrak : 

Teknologi biometrik memanfaatkan karakteristik fisik atau perilaku manusia untuk identifikasi dan verifikasi identitas, dengan salah satu implementasi paling signifikan adalah biometrik iris. Teknologi ini menggunakan pola unik pada iris mata untuk tujuan identifikasi yang aman dan andal, namun masih menghadapi tantangan dalam memastikan segmentasi citra yang konsisten. Penelitian ini berfokus pada pengembangan segmentasi citra iris menggunakan deep learning sebagai langkah krusial dalam proses identifikasi biometrik iris. Segmentasi citra bertujuan untuk memisahkan wilayah iris dari bagian mata lainnya, seperti pupil, sklera, dan kelopak mata, namun proses ini memerlukan pendekatan yang lebih canggih untuk mengatasi variasi citra. Penelitian ini mengimplementasikan arsitektur deep learning populer, yaitu DeepLabV3 dan U-Net, untuk segmentasi citra iris. Evaluasi performa dilakukan menggunakan metrik IoU Score, Accuracy, Precision, Recall, dan F1-Score. Hasil pengujian menunjukkan bahwa DeepLabV3 memberikan kinerja terbaik dengan IoU Score sebesar 0,918, Accuracy sebesar 0,993, Precision sebesar 0,962, Recall sebesar 0,952, dan F1-Score sebesar 0,957. Keunggulan DeepLabV3 terletak pada kemampuannya dalam melakukan ekstraksi fitur yang kompleks dan menangkap konteks informasi pada berbagai skala secara efektif. Temuan ini menggarisbawahi potensi besar penerapan deep learning dalam segmentasi citra iris untuk sistem biometrik. Dengan performa optimal yang dicapai oleh DeepLabV3, teknologi ini dapat diandalkan untuk meningkatkan akurasi dan efisiensi proses identifikasi biometrik, membuka peluang luas untuk pengembangan lebih lanjut dalam aplikasi keamanan berbasis iris.

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Abstract :

Biometric technology is an innovation that uses human physical or behavioral characteristics for identity determination and verification with an aspect of its most significant implementations identified to be iris biometrics. The technology uses unique patterns in iris for secure and reliable identification purposes but certain challenges are encountered in ensuring consistent image segmentation. Therefore, this research focuses on developing iris image segmentation using deep learning as an important step in biometric identification process. Image segmentation aims to separate iris region from other parts of the eye, such as the pupil, sclera, and eyelids. However, the process requires a more sophisticated method to overcome image variations. This research implements popular deep learning architectures, DeepLabV3 and U-Net, for the segmentation. Subsequently, the performance of the models was evaluated based on the IoU Score, accuracy, precision, recall, and F1-score metrics. The results showed that DeepLabV3 provided the best performance with an IoU Score of 0.918, accuracy of 0.993, precision of 0.962, recall of 0.952, and F1-score of 0.957. The advantage of the model was associated with the ability to effectively extract complex features and capture information context at different scales. The observation was an indication of the significant potential possessed by deep learning applications in iris image segmentation for biometric systems. Moreover, the optimal performance achieved by DeepLabV3 showed the possibility of depending on the technology to improve the accuracy and efficiency of biometric identification process, opening up broad opportunities for further development in iris-based security applications.

Keywords

Biometrics, Deep learning, Deeplabv3, Image segmentation, Iris, U-net

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References

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DOI: https://doi.org/10.20961/ijai.v9i2.100566

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