An efficient way to extract, and compare and match fingerprint minutiae

I am currently working on an program that detects and matches fingerprints as part of a fingerprint sensor. After processing the image, I obtain key points using Harris Corner Detection. Then, using ORB feature extractor, I obtain descriptors in the form of an array.

Problem is the number of key points I get for two different images of the same fingerprint are different. Hence, the descriptor arrays obtained are also of different sizes.

Now I've used Hamming distances to measure the difference between the descriptor arrays of two images, and hence the difference between the fingerprints themselves. However, due to the different array sizes, I'm finding it difficult to set a threshold for all fingerprints.

def get_descriptors(img):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
img = clahe.apply(img)
img = image_enhance.image_enhance(img) #for image-processing
img = numpy.array(img, dtype=numpy.uint8)

# Threshold
ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
# Normalize to 0 and 1 range
img[img == 255] = 1

#Thinning
skeleton = skeletonize(img)
skeleton = numpy.array(skeleton, dtype=numpy.uint8)
skeleton = removedot(skeleton)
# Harris corners
harris_corners = cv2.cornerHarris(img, 3, 3, 0.04)
harris_normalized = cv2.normalize(harris_corners, 0, 255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32FC1)
threshold_harris = 125
# Extract keypoints
keypoints = []
for x in range(0, harris_normalized.shape[0]):
    for y in range(0, harris_normalized.shape[1]):
        if harris_normalized[x][y] > threshold_harris:
            keypoints.append(cv2.KeyPoint(y, x, 1))
# Define descriptor
orb = cv2.ORB_create()
# Compute descriptors
_, des = orb.compute(img, keypoints)

return (keypoints, des);

def main():

img1 = cv2.imread("C:/Users/Nimesh Shahdadpuri/Desktop/DMRC Intern/database/106_1.tif" , cv2.IMREAD_GRAYSCALE)

kp1, des1 = get_descriptors(img1)

#print (des1)   
#print (des1.shape)
img2 = cv2.imread("C:/Users/Nimesh Shahdadpuri/Desktop/DMRC Intern/database/106_2.tif" , cv2.IMREAD_GRAYSCALE)
kp2, des2 = get_descriptors(img2)
#print (des2)

# Matching between descriptors
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches= bf.match(des1,des2)
matches = sorted(matches, key= lambda match:match.distance)
#print (len(matches))
# Plot keypoints
img4 = cv2.drawKeypoints(img1, kp1, outImage=None)
img5 = cv2.drawKeypoints(img2, kp2, outImage=None)
#f, axarr = plt.subplots(1,2)
print ("First Fingerprint")
#axarr[0].imshow(img4) 
plt.imshow(img4)
plt.show()
print ("Second Fingerprint")
#axarr[1].imshow(img5)
plt.imshow(img5)
plt.show()
# Plot matches
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches, flags=2, outImg=None)
print ("All the matching points and the corresponding distances")
plt.imshow(img3)
plt.show()

# Calculate score
score = 0
for match in matches:
    score += match.distance
score_threshold = 40
matchper= score/len(matches)
print(matchper)     

if  matchper < score_threshold:
    print("Fingerprint matches.")
else:
    print("Fingerprint does not match.")

I expect an efficient way to define a general threshold for all fingerprints. I would also like suggestions for an alternate approach to define and match the key points.