Keras: Load dataset and autocrop relevant area of image

Organize your directories as follows

main_dir
-train_dir
``person1_fake_dir
```person1 fake image
```person1 fake image
---etc
``person1_real_dir
---person1 real image
---person1 real image
--- etc
--person2_fake_dir
--- person2 fake image
--- person2 fake image
--- etc
--person2_real_dir
---person2 real image
---person2 real image
---etc
.
.
.
--personN_fake_dir
---personN fake image
---personN fake image
---etc
--personN_real_dir
---personN real image
---personN real image
--- etc

-test_dir
same structure as train_dir but put test images here

-valid_dir
same structure as train_dir but put validation images here

If you have N persons then you will have 2 X N classes

You can then use tf.keras.preprocessing.image.ImageDataGenerator().flow_from_directory() to input your data. Documentation is here. You don't have to worry about cropping the images just set the image size in flow to something like (256,256). Code below show the rest of the code you need

data_gen=tf.keras.preprocessing.image.ImageDataGenerator(resize=1/255)
train_gen=data_gen.flow_from_directory(train_dir, target_size=(224,224), color-mode='grayscale')
valid_gen=data_gen.flow_from_directory(valid_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
test_gen=data_gen.flow_from_directory(test_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.CategoricalCrossentropy(), metrics='accuracy')
history=model.fit(train_gen, epochs=20, verbose=1)
accuracy=model.evaluate (test_gen)[1]*100
print ('Model accuracy is ', accuracy)

Note your model will not be able to tell fake from real in the general case. It should work for persons 1 through N. You could try putting all the fake images in one class directory and all the real images in another class directory and train it but I suspect it will not work well in telling real from fake for the general case.