Persian Signature Verification using Fully Convolutional Networks
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ELCM02_141
تاریخ نمایه سازی: 21 اردیبهشت 1397
Abstract:
Fully convolutional networks (FCNs) have been recently used for feature extraction and classification in image and speech recognition, where their inputs have been raw signal or other complicated features. Persian signature verification is done using conventional convolutional neural networks (CNNs). In this paper, we propose to use FCN for learning a robust feature extraction from the raw signature images. FCN can be considered as a variant of CNN where its fully connected layers are replaced with a global pooling layer. In the proposed manner, FCN inputs are raw signature images and convolution filter size is fixed. Recognition accuracy on UTSig database, shows that FCN with a global average pooling outperforms CNN.
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Authors
Mohammad Rezaei
Department of computer engineering, K.N.Toosi University of Technology, Tehran, Iran
Nader Naderi
Department of engineering, Islamic Azad University Arak Branch, Iran