基于粒子群算法的ANN生物特征通信评价与身份识别

N. Umasankari, B. Muthukumar
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摘要

本研究探讨了提供生物特征图像详细信息的新技术,以及应用于生物特征图像预处理的方法。提出了一种基于优化粒子群算法(PSO)和人工神经网络(ANN)的属性分类方法。在目前的工作中,人们一直在努力设计一种高效的生物特征图像识别技术,特别是指纹和视网膜图像的识别技术。首先,预处理模块采用直方图均衡化的方法对整个图像进行对比度增强,以获得最佳的图像质量。这使得图像能够适应进一步的处理。接下来,特征提取模块涉及两个图像集(指纹和视网膜图像)。该模块采用灰度共生矩阵(GLCM)提取所需特征。其次是基于特征的融合技术(FBFT),用于减少用于身份验证的特征。本研究利用FBFT得到融合特征向量。最后,讨论了图像的非识别和识别问题。利用人工神经网络(ANN)对图像进行检测。其中,识别由人工神经网络完成,优化由粒子群优化算法(PSOA)完成。人工神经网络将图像分类为可识别和不可识别,并产生最佳结果。
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Evaluation of biometric communication and authenticate recognition using ANN with PSO algorithm
This research investigates the novel techniques which provide the detailed information on the biometric images used along with the methods applied for biometric image pre-processing. It also describes the proposed methodology which was implemented with the method of optimized Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) algorithm for classification of attributes. In the current work, a big effort has been implemented for designing an efficient technique for recognizing the biometric images, especially for the modalities like finger print and retina image. Initially, the pre-processing module used the method of histogram equalization to enhance the contrasts of entire image in order to get the best image quality. This makes the image adaptable for further processing. Next, the feature extraction module has the involvement of two image sets (finger print and retina image). The Gray Level Co-occurrence Matrix (GLCM) was used for extracting the needed features in this module. Next is Feature Based Fusion Technique (FBFT) for reducing the features for authentication purpose. This research work uses the FBFT to get fused feature vector. Finally, deals with the non-recognition and recognition of the images. The images were tested by using Artificial Neural Network (ANN). Here, the recognition is done by ANN and the optimization is done by the sophisticated function of Particle Swarm Optimization Algorithm (PSOA). ANN does the classification of images as recognized and non-recognized and yields best results.
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