Implementing the local ridge orientation

Question

I've been trying to implement the local ridge orientation for fingerprints in python. I've used the Gradient method, and using sobel operator to get the gradients I need. However it turned out that this method has quite a lot of flaws, especially around 90 degrees. I could include the code that I've done so far, but as it does not work as I want, I don't know if it's needed. I've also looked at the line segment method, however, I'm working with latent fingerprints so it is hard to know if one should look for maximum of black or white in the line segments. I've also tried to implement an algorithm to detect the area of maximum concentration of continous lines, but I couldn't get this to work. Any suggestion for other algorithms to use?

EDIT:

I'm using a function to apply my function to blocks, but that is hardly relevant

def lro(im_np):

    orientsmoothsigma = 3

    Gxx = cv2.Sobel(im_np,-1,2,0)
    Gxy = cv2.Sobel(im_np,-1,1,1)
    Gyy = cv2.Sobel(im_np,-1,0,2)


    Gxx = scipy.ndimage.filters.gaussian_filter(Gxx, orientsmoothsigma)
    Gxy = numpy.multiply(scipy.ndimage.filters.gaussian_filter(Gxy, orientsmoothsigma), 2.0)
    Gyy = scipy.ndimage.filters.gaussian_filter(Gyy, orientsmoothsigma)

    denom = numpy.sqrt(numpy.add(numpy.power(Gxy,2), (numpy.power(numpy.subtract(Gxx,Gyy),2))))# + eps;
    sin2theta = numpy.divide(Gxy,denom)            # Sine and cosine of doubled angles
    cos2theta = numpy.divide(numpy.subtract(Gxx,Gyy),denom)

    sze = math.floor(6*orientsmoothsigma);
    if not sze%2: sze = sze+1       
    cos2theta = scipy.ndimage.filters.gaussian_filter(cos2theta, orientsmoothsigma)  # Smoothed sine and cosine of
    sin2theta = scipy.ndimage.filters.gaussian_filter(sin2theta, orientsmoothsigma)#filter2(f, sin2theta); # doubled angles



    orientim = math.pi/2. + numpy.divide(numpy.arctan2(sin2theta,cos2theta),2.)

    return orientim

Answer 1

I worked on this a long time ago, and wrote a paper on it. As I remember, look for both black and white ridges (invert the image and repeat the analysis) to give more results. I do remember some sensitivity at some angles. You probably need something with more extent than a pure Sobel. Try to reach out as many pixels as practical.

Answer 2

You may want to have a look at the work of Raymond Thai (Fingerprint Image Enhancement and Minutiae Extraction), if you haven't already.