Abstract
Image classification is a process of categorizing images based on features. Features in an image could be identified as a change in pixel intensity or an edge. A color image has the pixel values represented using R, G, and B. Multiple such images are labeled and used for image classification. The challenging part is to identify the features in which such images are a complex task. CNN is a widely used image processing algorithm particularly for image classification. The three layers of CNN—convolution layer, pooling layer, and fully connected layers—can be applied to an image for image processing problems such as image recognition, object detection, and segmentation. In the proposed system, CNN is applied on patch-based sclera–periocular images. The model has shown an accuracy of 99.3% for patch-based images. The model was trained on image patches of size 100 × 100, 50 × 50, and 25 × 25.
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Acknowledgements
We like to express gratitude to the University of Missouri-Kansas City for providing access to the VISOB data set and the Department of Computer Science, the University of Beira Interior, for providing access to the UBIPr and SBVPI data set.
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Sandhya, V., Hegde, N.P. (2023). Performance Analysis of CNN for Patch-Based Sclera–Periocular Biometrics. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_8
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