Keras Deep Learning for Pupil Detection Method

Authors

  • Renas Rajab Asaad

DOI:

https://doi.org/10.25007/ajnu.v10n4a1328

Keywords:

Machine Learning, Deep Learning, MTCNN, Keras Model, AI

Abstract

Nowadays, deep learning is the most common field in artificial intelligence, deep learning is a technique invented by man in order to try to imitate the way the human mind works. Deep learning tries to simulate the human mind in all its capabilities, which include; Seeing, understanding speech, composing it, hearing, and other powerful abilities that our human mind possesses and is not rivaled by anything else, or so it was. The matter did not stop at that only, but that scientists have studied the human brain and how it works in order to design algorithms and programs capable of simulating it, and for this reason we find that these algorithms are inspired by medical and neurological studies of humans and try as much as possible to imitate them, but by computer methods. Not biological. In this paper studied and practiced the multi-task deep convolution neural network(MTCNN) for face detection after detecting the faces its try to extract eyes from image, finally pupil detector works by using Keras model in python. The result of this paper shows the power of deep learning field biologically comparative with human thinks.

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References

Hoang, A. T., Nižetić, S., Ong, H. C., Tarelko, W., Le, T. H., Chau, M. Q., & Nguyen, X. P. (2021). A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustainable Energy Technologies and Assessments, 47, 101416.

Moldwin, T., & Segev, I. (2020). Perceptron learning and classification in a modeled cortical pyramidal cell. Frontiers in computational neuroscience, 14, 33.

Ramasubramanian, K., & Singh, A. (2019). Deep learning using keras and tensorflow. In Machine Learning Using R (pp. 667-688). Apress, Berkeley, CA.

Vasilev, I., Slater, D., Spacagna, G., Roelants, P., & Zocca, V. (2019). Python Deep Learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow. Packt Publishing Ltd.

Ghofrani, A., Toroghi, R. M., & Ghanbari, S. (2019). Realtime face-detection and emotion recognition using mtcnn and minishufflenet v2. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (pp. 817-821). IEEE.

Rajab Asaad, R. (2021). Review on Deep Learning and Neural Network Implementation for Emotions Recognition . Qubahan Academic Journal, 1(1), 1–4. https://doi.org/10.48161/qaj.v1n1a25

Asaad, R. R., & Ali, R. I. (2019). Back Propagation Neural Network(BPNN) and Sigmoid Activation Function in Multi-Layer Networks. Academic Journal of Nawroz University, 8(4), 216–221. https://doi.org/10.25007/ajnu.v8n4a464

Chen, X., Luo, X., Liu, X., & Fang, J. (2019, May). Eyes localization algorithm based on prior MTCNN face detection. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 1763-1767). IEEE.

Manaswi, N. K. (2018). Understanding and working with Keras. In Deep Learning with Applications Using Python (pp. 31-43). Apress, Berkeley, CA.

Published

2022-02-07

How to Cite

Rajab Asaad, R. (2022). Keras Deep Learning for Pupil Detection Method . Academic Journal of Nawroz University, 10(4), 240–250. https://doi.org/10.25007/ajnu.v10n4a1328

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