diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index def1d0c..f0832b3 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -3,15 +3,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans, getImageCrop, Denoiser
-import sys
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans, getImageCrop, Denoiser, Distance
 import os
 import random
 
-sys.path.insert(0, '../../algorithms/distance/')
-
-from rms_diff import rmsDiffNumpy
-
 # Configuration:
 
 DENOISER = Denoiser.MEAN
@@ -21,6 +16,17 @@ IMAGES_CAMERAS_FOLDER = {
 }
 TRAINING_PORTION = 0.5
 PREDICT_ONLY_ON_WHOLE_TRAINING_SET = False
+DISTANCE = Distance.ROOT_MEAN_SQUARE
+
+# See [Robust_image_source_identification_on_modern_smartphones/issues/22](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/22).
+match DISTANCE:
+    case Distance.ROOT_MEAN_SQUARE:
+        import sys
+        sys.path.insert(0, '../../algorithms/distance/')
+        from rms_diff import rmsDiffNumpy
+    case PEARSON_CORRELATION:
+        import scipy
+        from utils import getPearsonCorrelation
 
 setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
 
@@ -180,7 +186,11 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
                                 cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
-                            distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
+                            match DISTANCE:
+                                case ROOT_MEAN_SQUARE:
+                                    distance = rmsDiffNumpy(cameraTestingImageNoise, camerasIterativeMean[camera].mean)
+                                case PEARSON_CORRELATION:
+                                    distance = getPearsonCorrelation(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
                             print(f'{cameraTrainingImageIndex=} {cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
                             if minimalDistance is None or distance < minimalDistance:
                                 minimalDistance = distance
diff --git a/datasets/raise/split_and_compare_prnus_of_subgroups.py b/datasets/raise/split_and_compare_prnus_of_subgroups.py
index 0348d87..a42bdc5 100755
--- a/datasets/raise/split_and_compare_prnus_of_subgroups.py
+++ b/datasets/raise/split_and_compare_prnus_of_subgroups.py
@@ -3,18 +3,24 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, Denoiser
-import sys
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, Denoiser, Distance
 import os
 import random
 
-sys.path.insert(0, '../../algorithms/distance/')
-
-from rms_diff import rmsDiffNumpy
-
 NUMBER_OF_SUBGROUPS = 2
 DENOISER = Denoiser.WAVELET
 IMAGES_FOLDER = 'flat-field/NEF'
+DISTANCE = Distance.ROOT_MEAN_SQUARE
+
+# See [Robust_image_source_identification_on_modern_smartphones/issues/22](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/22).
+match DISTANCE:
+    case Distance.ROOT_MEAN_SQUARE:
+        import sys
+        sys.path.insert(0, '../../algorithms/distance/')
+        from rms_diff import rmsDiffNumpy
+    case PEARSON_CORRELATION:
+        import scipy
+        from utils import getPearsonCorrelation
 
 setting = escapeFilePath(IMAGES_FOLDER) + f'_{DENOISER}'
 
@@ -26,7 +32,7 @@ random.shuffle(imagesFileNames)
 numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
 
 subgroupsIterativeMean = [iterativeMean() for _ in range(NUMBER_OF_SUBGROUPS)]
-rmss = []
+distances = []
 
 minColor = None
 maxColor = None
@@ -55,16 +61,22 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
             subgroupIterativeMean.add(imagePrnuEstimateNpArray)
             if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
                 assert NUMBER_OF_SUBGROUPS == 2
-                rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
-                rmss += [rms]
+                firstImage = subgroupIterativeMean.mean
+                secondImage = subgroupsIterativeMean[1 - subgroupIndex].mean
+                match DISTANCE:
+                    case ROOT_MEAN_SQUARE:
+                        distance = rmsDiffNumpy(firstImage, secondImage)
+                    case PEARSON_CORRELATION:
+                        distance = getPearsonCorrelation(firstImage, secondImage)
+                distances += [distance]
 
 for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
     plt.imsave(f'{setting}_estimated_prnu_subgroup_{subgroupIndex}.png', (subgroupsIterativeMean[subgroupIndex].mean))
 
-plt.title(f'RMS between both subgroups estimated PRNUs with {DENOISER} denoiser for a given number of images among them')
+plt.title(f'{DISTANCE.getEndUserName()} between both subgroups estimated PRNUs with {DENOISER} denoiser for a given number of images among them')
 plt.xlabel('Number of images of each subgroup')
-plt.ylabel('RMS between both subgroups estimated PRNUs')
-plt.plot(rmss)
-saveNpArray(f'{setting}_rmss', rmss)
-plt.savefig(f'{setting}_rms_between_estimated_prnu_of_2_subgroups.svg')
+plt.ylabel(f'{DISTANCE.getEndUserName()} between both subgroups estimated PRNUs')
+plt.plot(distances)
+saveNpArray(f'{setting}_{DISTANCE}s', distances)
+plt.savefig(f'{setting}_{DISTANCE}_between_estimated_prnu_of_2_subgroups.svg')
 #plt.show()
diff --git a/datasets/raise/utils.py b/datasets/raise/utils.py
index 6c699a0..60c802f 100644
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -33,6 +33,19 @@ class Denoiser(Enum):
     def __str__(self):
         return self.name.lower()
 
+class Distance(Enum)
+    ROOT_MEAN_SQUARE = auto()
+    PEARSON_CORRELATION = auto()
+
+    def __str__(self):
+        return self.name.lower()
+
+    def getEndUserName(self):
+        return self.name.replace('_', '').title()
+
+def getPearsonCorrelation(firstImage, secondImage):
+    return abs(scipy.stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic - 1)
+
 # Source: https://stackoverflow.com/a/43346070
 def getGaussianKernel(size, sigma):
     axis = np.linspace(-(size - 1) / 2., (size - 1) / 2., size)