Abstract
There are some benefits in using periocular biometric traits for individual identification. This work describes the use of convolutional neural network Neocognitron, in this novel application, in individual recognition using periocular region images. Besides, it is used the competitive learning using the extreme points of lines detected in the preprocessing of the input images as winner positions. It was used Carnegie Mellon University - Pose, Illumination, and Expression Database (CMU-PIE), with 41,368 images of 68 persons. From these images, 57 \(\times \) 57 periocular images were obtained as training and test samples. The experiments indicate results in the Kappa index of 0.89, for periocular images, and 0.91 for complete face images.
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Acknowledgements
The authors would like to thank Prof. Simon Baker from Carnegie Mellon University for the kindness of sending the PIE database, used in this work, and to acknowledge CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), Ministry of Education, Brazil, Financing Code 001.
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da Silva, E.P., Fambrini, F., Saito, J.H. (2020). Convolutional Neural Networks and Periocular Region Image Recognition. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_36
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DOI: https://doi.org/10.1007/978-3-030-63820-7_36
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