diff --git a/PersonDatasetAssembler/PersonDatasetAssembler.py b/PersonDatasetAssembler/PersonDatasetAssembler.py
index 8b365a5..e4385c5 100755
--- a/PersonDatasetAssembler/PersonDatasetAssembler.py
+++ b/PersonDatasetAssembler/PersonDatasetAssembler.py
@@ -25,7 +25,7 @@ from typing import Iterator
 import cv2
 import numpy
 from tqdm import tqdm
-from wand.exceptions import BlobError
+from wand.exceptions import BlobError, CoderError
 from wand.image import Image
 
 image_ext_ocv = [".bmp", ".jpeg", ".jpg", ".png"]
@@ -41,7 +41,7 @@ def find_image_files(path: str) -> list[str]:
 	for root, dirs, files in os.walk(path):
 		for filename in files:
 			name, extension = os.path.splitext(filename)
-			if extension.lower() in image_ext_ocv or extension in image_ext_wand:
+			if extension.lower() in image_ext_ocv or extension.lower() in image_ext_wand:
 				paths.append(os.path.join(root, filename))
 	return paths
 
@@ -58,10 +58,20 @@ def image_loader(paths: list[str]) -> Iterator[numpy.ndarray]:
 				yield image
 		elif extension in image_ext_wand:
 			try:
-				image = Image(filename=path)
+				wandImage = Image(filename=path)
+				wandImage.auto_orient()
+				image = numpy.array(wandImage)
+				image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+				yield image
 			except BlobError as e:
 				print(f"Warning: could not load {path}, {e}")
 				continue
+			except CoderError as e:
+				print(f"Warning: failure in wand while loading {path}, {e}")
+
+		else:
+			print(f"Warning: could not load {path}, {e}")
+			continue
 
 
 def extract_video_images(video: cv2.VideoCapture, interval: int = 0):
@@ -132,7 +142,7 @@ if __name__ == "__main__":
 
 	recognizer = cv2.FaceRecognizerSF.create(model=args.match_model, config="", backend_id=cv2.dnn.DNN_BACKEND_DEFAULT , target_id=cv2.dnn.DNN_TARGET_CPU)
 	detector = cv2.FaceDetectorYN.create(model=args.detect_model, config="", input_size=[320, 320],
-		score_threshold=0.6, nms_threshold=0.3, top_k=5000, backend_id=cv2.dnn.DNN_BACKEND_DEFAULT, target_id=cv2.dnn.DNN_TARGET_CPU)
+		score_threshold=0.4, nms_threshold=0.2, top_k=5000, backend_id=cv2.dnn.DNN_BACKEND_DEFAULT, target_id=cv2.dnn.DNN_TARGET_CPU)
 
 	referance_features = process_referance(detector, recognizer, args.referance)
 	if len(referance_features) < 1:
@@ -166,7 +176,7 @@ if __name__ == "__main__":
 			resized = image
 		score, match = contains_face_match(detector, recognizer, resized, referance_features, args.threshold)
 		if match and not args.invert or not match and args.invert:
-			filename = f"{counter:04}.png"
+			filename = f"{counter:04}.jpg"
 			cv2.imwrite(os.path.join(args.out, filename), image)
 			counter += 1
 		progress.set_description(f"{score:1.2f}")