Getting Error at 6th Cell of LFW.ipynb

[FahimT5]

I'm getting a value error at the 6th cell of LFW.ipynb. I'm getting this error both in google colab and my local machine. Please solve this issue.

The error is:

ValueError Traceback (most recent call last)
in
11
12 #obj = DeepFace.verify(img1, img2, model_name = 'VGG-Face', model = vgg_model)
---> 13 obj = DeepFace.verify(img1, img2, model_name = 'Dlib', model = dlib_model, distance_metric = 'euclidean')
14 prediction = obj["verified"]
15 predictions.append(prediction)

~/.local/lib/python3.8/site-packages/deepface/DeepFace.py in verify(img1_path, img2_path, model_name, distance_metric, model, enforce_detection, detector_backend)
152 , detector_backend = detector_backend)
153
--> 154 img2 = functions.preprocess_face(img=img2_path
155 , target_size=(input_shape_y, input_shape_x)
156 , enforce_detection = enforce_detection

~/.local/lib/python3.8/site-packages/deepface/commons/functions.py in preprocess_face(img, target_size, grayscale, enforce_detection, detector_backend)
454
455 if enforce_detection == True:
--> 456 raise ValueError("Detected face shape is ", img.shape,". Consider to set enforce_detection argument to False.")
457 else: #restore base image
458 img = base_img.copy()

ValueError: ('Detected face shape is ', (0, 92, 3), '. Consider to set enforce_detection argument to False.')

[serengil]

No face found on one of your face pair. Getting this error is totally normal. Why don't you set the enforce_detection argument to False as mentioned in the message?

obj = DeepFace.verify(img1, img2, model_name = 'Dlib', model = dlib_model, distance_metric = 'euclidean', enforce_detection = False)

This will solve your issue but setting enforce_detection to False will decrease the accuracy dramatically. Try to feed high resolution images.

[FahimT5]

Thanks for your suggestion. Now after putting enforce_detection = False, new error appears.

Now showing TypeError: Cannot handle this data type: (1, 1, 3), <f4.

How can I feed high-resolution images? Is there any way to filter out small images? As this is a fixed dataset by scikit learn. Can I manually select my dataset folder or any other way?