LinuxARMLinux指纹门禁系统.ppt

int ret = 0; unsigned char tmp; if(_IOC_TYPE(cmd) != FPS200_IOC_MAGIC) return -ENOTTY; if(_IOC_NR(cmd) > FPS200_IOC_MAXNR) return -ENOTTY; if (_IOC_DIR(cmd) & _IOC_READ) err = verify_area(VERIFY_WRITE, (void *)arg, _IOC_SIZE(cmd)); else if (_IOC_DIR(cmd) & _IOC_WRITE) err = verify_area(VERIFY_READ, (void *)arg, _IOC_SIZE(cmd)); if (err) return err; switch(cmd) { case FPS200_IOCSDTR: ret = __get_user(tmp, (unsigned char *)arg); if(tmp > 0x7f) tmp = 0x7f; FPS_INDEX = FPS_DTR; FPS_DATA = tmp; break; case FPS200_IOCSDCR: ret = __get_user(tmp, (unsigned char *)arg); if(tmp > 0x1f) tmp = 0x1f; FPS_INDEX = FPS_DCR; FPS_DATA = tmp; break; …… } 1、指纹图像增强(Gabor滤波法) 2、指纹图像的细化 3、指纹图像的特征点提取 4、指纹图像的特征匹配 14.6.3 指纹识别算法的实现 1. 指纹图像增强(Gabor滤波法)。构造Gabor滤波器： float dy2 = 1.0/r2; float dx2 = 1.0/r2; float x2, y2; y2 = -x*sin(phi) + y*cos(phi); x2 = x*cos(phi) + y*sin(phi); return exp(-0.5*(x2*x2*dx2 + y2*y2*dy2))*cos(2*pi*x2*f); 用Gabor滤波器进行滤波： for (j = Wg2; j < lHeight-Wg2; j++) for (i = Wg2; i < lWidth-Wg2; i++) {sum = 0.0; o = orientation[i+j*lLineBytes]; f = frequence[i+j*lLineBytes]; for (v = -Wg2; v <= Wg2; v++) for (u = -Wg2; u <= Wg2; u++) {sum += EnhanceGabor( (float)u, (float)v,o,f,radius) * (*(lpSrc+(j-v)*lLineBytes+i-u));} if (sum>255.0) sum = 255.0; if (sum<0.0) sum = 0.0; *( enhanced+j*lLineBytes+i) = (unsigned char)sum; }} 2. 指纹图像的细化。采用经典形态学细化方法，程序如下： //逐个判断条件。 //判断2<=NZ(P1)<=6 nCount = neighbour[1][1] + neighbour[1][2] + neighbour[1][3] \ + neighbour[2][1] + neighbour[2][3] + \ + neighbour[3][1] + neighbour[3][2] + neighbour[3][3]; if ( nCount >= 2 && nCount <=6) bCondition1 = TRUE; //判断Z0(P1)=1 nCount = 0; if (neighbour[1][2] == 0 && neighbour[1][1] == 1) nCount++; if (neighbour[1][1] == 0 && neighbour[2][1] == 1) nCount++; if (neighbour[2][1] == 0 && neighbour[3][1] == 1) nCount++; if (neighbour[3][1] == 0 && neighbour[3][2] == 1) nCount++; if (neighbour[3][2] == 0 && neighbour[3][3] == 1) nCount++;