Convolutional Neural Networks for Pattern Recognition

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The Convolutional Neural Network (CNN), a variant of the Multilayer Perception (MLP), has shown promise in solving complex recognition problems, particularly in visual pattern recognition. However, the classical LeNet-5 CNN model, which most solutions are based on, is highly compute-intensive. This CNN also suffers from long training time, due to the large number of layers that ranges from six to eight. In this research, a CNN model with a reduced complexity is proposed for application in face recognition and finger-vein biometric identification.

Researcher :  UTEM Press SKU: utem-1 Category: Tags: , , , ,

Additional information

Type

Hardcopy
Author

Syafeeza Ahmad Radzi
ISBN

978-967-0257-80-8
Delivery

1-2 weeks for Malaysia
5-8 weeks for Worldwide

UTem Press was established in October 2002 under the responsibility of the Chancellery Office publishes materials and print graphics for promotional purposes of the university. On 1 November 2004, it was promoted as a Centre of Responsibility (CoR) and serves as One Stop-Centre to publish educational materials, product graphics and multimedia products of the university. In 2010 a new restructuring occurred along with the development in the field of publication where we were divided into two parts, the Division of Administration and the Division of Editorial and Marketing. While the editorial division has been divided into 5 units. In 2015, University Press has expanded its services with the 7 main service components for university needs.

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