基于加权强度PCNN模型的分块人脸识别  

Block face recognition using PCNN model based on weighted strength

作　　者：李瀚[1] 扆梦楠 邓红霞[1] 常莎[1] 李海芳[1] 

机构地区：[1]太原理工大学计算机科学与技术学院,山西晋中030600

出　　处：《计算机工程与设计》2017年第9期2525-2529,2572,共6页Computer Engineering and Design

基　　金：国家自然科学基金项目(61472270);山西省自然科学(青年科技研究)基金项目(2014021022-5)

摘　　要：为提高利用脉冲耦合神经网络(pulse coupled neural network,PCNN)进行人脸识别时的准确率,提出基于加权强度PCNN模型的分块人脸识别方法。在简化PCNN模型的基础上,引入自发脉冲发放强度、耦合脉冲发放强度和加权强度的概念,细化模型的输出;根据人脸图像各部分灰度分布的不同和局部识别率的不同,将人脸图像进行分块;进行人脸识别时,分块的权值会根据分块图像的局部信息熵自适应地设定,模型的参数会根据分块图像内容设定,一幅人脸图像的识别结果会综合各分块的识别结果。多个数据库上的实验结果表明,该算法可以有效地提高识别率。To raise the accuracy of face recognition using pulse coupled neural network （PCNN） model, the block face recogni-tion method using PCNN model based on weighted strength was proposed Based on the simplified PCNN model, spontaneous pulse strength, coupled pulse strength and weighted strength were proposed, which made the output of the model more accu-rate. Each face image was divided into several block images according to the recognition rate and gray distribution of different block images. The weight corresponding to one block image was set self-adaptively according to its local entropy. Parameters of PCNN model were also set according to different block images. All recognition results corresponding to all block images of one face image were comprehensively considered to get the final recognition result. Experimental results show that the proposed method can improve face recognition rate effectively.

关 键 词：脉冲耦合神经网络 自发脉冲发放强度 耦合脉冲发放强度 加权强度 人脸识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]