Use the best match for better accuracy in examples

examples/facerec_from_webcam.py

 import face_recognition
 import cv2
+import numpy as np
 
 # This is a super simple (but slow) example of running face recognition on live video from your webcam.
 # There's a second example that's a little more complicated but runs faster.
@@ -48,9 +49,15 @@ while True:
         name = "Unknown"
 
         # If a match was found in known_face_encodings, just use the first one.
-        if True in matches:
-            first_match_index = matches.index(True)
-            name = known_face_names[first_match_index]
+        # if True in matches:
+        #     first_match_index = matches.index(True)
+        #     name = known_face_names[first_match_index]
+
+        # Or instead, use the known face with the smallest distance to the new face
+        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+        best_match_index = np.argmin(face_distances)
+        if matches[best_match_index]:
+            name = known_face_names[best_match_index]
 
         # Draw a box around the face
         cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

examples/facerec_from_webcam_faster.py

 import face_recognition
 import cv2
+import numpy as np
 
 # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
 # other example, but it includes some basic performance tweaks to make things run a lot faster:
@@ -59,10 +60,16 @@ while True:
             matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
             name = "Unknown"
 
-            # If a match was found in known_face_encodings, just use the first one.
-            if True in matches:
-                first_match_index = matches.index(True)
-                name = known_face_names[first_match_index]
+            # # If a match was found in known_face_encodings, just use the first one.
+            # if True in matches:
+            #     first_match_index = matches.index(True)
+            #     name = known_face_names[first_match_index]
+
+            # Or instead, use the known face with the smallest distance to the new face
+            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+            best_match_index = np.argmin(face_distances)
+            if matches[best_match_index]:
+                name = known_face_names[best_match_index]
 
             face_names.append(name)
 

examples/identify_and_draw_boxes_on_faces.py

 import face_recognition
 from PIL import Image, ImageDraw
+import numpy as np
 
 # This is an example of running face recognition on a single image
 # and drawing a box around each person that was identified.
@@ -43,9 +44,15 @@ for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodi
     name = "Unknown"
 
     # If a match was found in known_face_encodings, just use the first one.
-    if True in matches:
-        first_match_index = matches.index(True)
-        name = known_face_names[first_match_index]
+    # if True in matches:
+    #     first_match_index = matches.index(True)
+    #     name = known_face_names[first_match_index]
+
+    # Or instead, use the known face with the smallest distance to the new face
+    face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+    best_match_index = np.argmin(face_distances)
+    if matches[best_match_index]:
+        name = known_face_names[best_match_index]
 
     # Draw a box around the face using the Pillow module
     draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))