无线电工程
無線電工程
무선전공정
Radio Engineering
2015年
10期
29-33
,共5页
实时掌纹识别%多分辨率分析%小波变换%摄像头
實時掌紋識彆%多分辨率分析%小波變換%攝像頭
실시장문식별%다분변솔분석%소파변환%섭상두
real-time palm-print identification%multi-resolution analysis%wavelet transform%camera
传统的掌纹识别系统大多以接触式为主, 且设备昂贵、 灵活性和移动性较差. 为了拓展掌纹识别技术的应用, 设计以网络摄像头和手机摄像头为采集端的非接触式掌纹识别系统. 获取掌纹图像时, 在摄像头的预览画面加限制轮廓, 解决在非接触式图像采集过程中因位置不固定而造成掌纹特征纹理图像产生偏移的问题. 在掌纹预处理阶段, 针对获取的掌纹图像特点, 实现一种能够精确定位掌纹特征分离点和截取感兴趣区域的预处理算法, 并通过小波变换实现掌纹图像的特征提取和特征匹配. 测试结果表明, 通过对400人不同掌纹图像的识别处理, BESTPAL掌纹识别系统达到较高的识别率.
傳統的掌紋識彆繫統大多以接觸式為主, 且設備昂貴、 靈活性和移動性較差. 為瞭拓展掌紋識彆技術的應用, 設計以網絡攝像頭和手機攝像頭為採集耑的非接觸式掌紋識彆繫統. 穫取掌紋圖像時, 在攝像頭的預覽畫麵加限製輪廓, 解決在非接觸式圖像採集過程中因位置不固定而造成掌紋特徵紋理圖像產生偏移的問題. 在掌紋預處理階段, 針對穫取的掌紋圖像特點, 實現一種能夠精確定位掌紋特徵分離點和截取感興趣區域的預處理算法, 併通過小波變換實現掌紋圖像的特徵提取和特徵匹配. 測試結果錶明, 通過對400人不同掌紋圖像的識彆處理, BESTPAL掌紋識彆繫統達到較高的識彆率.
전통적장문식별계통대다이접촉식위주, 차설비앙귀、 령활성화이동성교차. 위료탁전장문식별기술적응용, 설계이망락섭상두화수궤섭상두위채집단적비접촉식장문식별계통. 획취장문도상시, 재섭상두적예람화면가한제륜곽, 해결재비접촉식도상채집과정중인위치불고정이조성장문특정문리도상산생편이적문제. 재장문예처리계단, 침대획취적장문도상특점, 실현일충능구정학정위장문특정분리점화절취감흥취구역적예처리산법, 병통과소파변환실현장문도상적특정제취화특정필배. 측시결과표명, 통과대400인불동장문도상적식별처리, BESTPAL장문식별계통체도교고적식별솔.
The traditional palm print identification system are mostly of contact-type with expensive equipment,weak flexibility and mobility.In order to extend the application of palm print identification technology,the non-contact palm print identification system is designed,which uses webcams and mobile phone cameras to get images.When getting the images,by adding restrictions outlines to the image preview of cameras,the system can solve the problem of deviation of palm print feature texture image caused by the unstable palm positions in the collection of palm images.On the pre-processing stage,based on the collected features of palm print images,the system can realize a preprocessing algorithm,which can fix the palm print feature separation point accurately and intercept the interested area of palm print images.In addition,it can implement the feature extraction and matching by wavelet. The test result shows that BESTPAL palm print identification system has higher recognition by testing different palm images of 400 people.