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Palm vein recognition based on end-to-end convolutional neural network / 南方医科大学学报
Journal of Southern Medical University ; (12): 207-214, 2019.
Artículo en Chino | WPRIM | ID: wpr-772097
ABSTRACT
We propose a novel palm-vein recognition model based on the end-to-end convolutional neural network. In this model, the convolutional layer and the pooling layer were alternately connected to extract the image features, and the categorical attribute was estimated simultaneously via the neural network classifier. The classification error was minimized via the mini-batch stochastic gradient descent algorithm with momentum to optimize the feature descriptor along with the direction of the gradient descent. Four strategies including data augmentation, batch normalization, dropout, and L2 parameter regularization were applied in the model to reduce the generalization error. The experimental results showed that for classifying 500 subjects form PolyU database and a self-established database, this model achieved identification rates of 99.90% and 98.05%, respectively, with an identification time for a single sample less than 9 ms. The proposed approach, as compared with the traditional method, could improve the accuracy of palm vein recognition in clincal applications and provides a new approach to palm vein recognition.
Asunto(s)

Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Asunto principal: Venas / Algoritmos / Diagnóstico por Imagen / Bases de Datos Factuales / Redes Neurales de la Computación / Mano Tipo de estudio: Estudio diagnóstico Límite: Humanos Idioma: Chino Revista: Journal of Southern Medical University Año: 2019 Tipo del documento: Artículo

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Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Asunto principal: Venas / Algoritmos / Diagnóstico por Imagen / Bases de Datos Factuales / Redes Neurales de la Computación / Mano Tipo de estudio: Estudio diagnóstico Límite: Humanos Idioma: Chino Revista: Journal of Southern Medical University Año: 2019 Tipo del documento: Artículo