Jorge E. Zambrano Ibujés

Jorge E. Zambrano Ibujés

Ph.D. in Electrical Engineering, Universidad de Chile. Working on Image Processing and AI applied to biometrics and biomedical systems.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Deep Learning-Based Differential Diagnosis of Major Depression and Bipolar Disorder Using Microglia-Cellular Sensors and Patient-Derived Small Extracellular Vesicles

Published in Scientific Reports, 2026

A deep learning framework using microglial cells as biosensors and DenseNet121 to differentiate major depressive disorder, bipolar disorder, and healthy controls from fluorescence microscopy images of patient-derived extracellular vesicles.

Recommended citation: J. E. Zambrano, A. Luarte, J. Contreras, J. P. Perez, L. Yantén-Fuentes, M. Prieto, P. Lazcano, U. Wyneken and C. A. Perez, "Deep Learning-Based Differential Diagnosis of Major Depression and Bipolar Disorder Using Microglia-Cellular Sensors and Patient-Derived Small Extracellular Vesicles," in Scientific Reports, vol. 16, no. 1, p. 11679, 2026, doi: 10.1038/s41598-026-47476-9.
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Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs

Published in IEEE Access, 2024

An iris recognition method that enhances a pretrained backbone with anti-aliasing techniques to improve shift-invariance, combined with bit-shifting for robustness against pupil dilation.

Recommended citation: J. E. Zambrano, J. I. Pilataxi, C. A. Perez and K. W. Bowyer, "Iris Recognition Using an Enhanced Pre-Trained Backbone Based on Anti-Aliased CNNs," in IEEE Access, vol. 12, pp. 94570-94583, 2024, doi: 10.1109/ACCESS.2024.3425648.
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Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector

Published in IEEE Access, 2024

A neuroevolution method that accelerates architecture search by training a classifier on CNN architecture encodings as feature vectors, reducing evaluation cost within genetic algorithm-based search.

Recommended citation: J. I. Pilataxi, J. E. Zambrano, C. A. Perez and K. W. Bowyer, "Improved Search in Neuroevolution Using a Neural Architecture Classifier With the CNN Architecture Encoding as Feature Vector," in IEEE Access, vol. 12, pp. 11987-12000, 2024, doi: 10.1109/ACCESS.2024.3355804.
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Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching

Published in IEEE Access, 2022

A deep-learning approach to iris recognition that uses features from the low-level layers of pretrained CNNs without further training, combined with bit-shifting for robustness against pupil dilation.

Recommended citation: J. E. Zambrano, D. P. Benalcazar, C. A. Perez and K. W. Bowyer, "Iris Recognition Using Low-Level CNN Layers Without Training and Single Matching," in IEEE Access, vol. 10, pp. 41276-41286, 2022, doi: 10.1109/ACCESS.2022.3166910.
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A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks

Published in IEEE Access, 2020

A single-image 3D iris reconstruction method based on a depth-estimation CNN, trained on synthetic and real iris datasets, that improves recognition performance by 48% over the standard 2D iris code.

Recommended citation: D. P. Benalcazar, J. E. Zambrano, D. Bastias, C. A. Perez and K. W. Bowyer, "A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 98584-98599, 2020, doi: 10.1109/ACCESS.2020.2996563.
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Implementation of a WirelessHART Training System for Upgrading Industrial Automation

Published in IEEE Latin America Transactions, 2016

A hands-on training platform that emulates a WirelessHART industrial network for teaching wireless process-control concepts to engineering students.

Recommended citation: I. Escobar, E. Pruna, O. Chang, A. Navas, J. E. Zambrano and G. Avila, "Implementation of a WirelessHART Training System for Upgrading Industrial," in IEEE Latin America Transactions, vol. 14, no. 6, pp. 2663-2668, June 2016.
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Conference Papers

3D Iris Recognition Using Spin Images

Published in 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020

A 3D iris recognition method that applies spin image descriptors to three-dimensional iris surface models, enabling recognition robust to the geometric distortions present in standard 2D approaches.

Recommended citation: D. P. Benalcazar, D. A. Montecino, J. E. Zambrano, C. A. Perez and K. W. Bowyer, "3D Iris Recognition Using Spin Images," in 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1-8, 2020, doi: 10.1109/IJCB48548.2020.9304890.
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