摘要
指纹识别技术是当今应用最广泛的生物识别技术之一。在指纹识别过程中,图像处理、特征提取、匹配等过程数据量庞大,计算比较烦琐。BP神经网络具有良好的自学习能力、强大的分类能力和容错能力,将其应用到指纹识别中是可行的。为改进BP神经网络计算速度较慢,梯度下降法不能处理一些不可微传递函数的问题,采用粒子群算法对BP算法进行优化,提高了指纹识别的速度和准确度。
Fingerprint identification technology is one of the most widely used biological recognition technology today. In the process of fingerprint identification, image processing, feature extraction, matching process have large amounts of data to be addressed and the calculation is also very troublesome. The BP neural network has good self-learning ability, strong classification ability and fault tolerance and it is feasible to be used in fingerprint identification. Meanwhile, the BP neural network also contains some problems such as slow computing speed, the gradient descent method can＇t deal with non-differential transfer function. This paper adopts the particle swarm optimization to optimize BP algorithm and improves the speed and accuracy of fingerprint recognition.
出处
《科技广场》
2011年第7期40-42,共3页
Science Mosaic
关键词
指纹识别
BP神经网络
粒子群算法
Fingerprint Identification
BP Neural Network
Particle Swarm Optimization
