Questions on using DeepFace.find() but with a custom face-detection

For my use case (a live-cam-analysis-setting) I have a face detection model already, but still need a good face-recognition model and I’m using DeepFace.find(): DeepFace.find(face_image, db_path = "./train_cut_faces/", model_name = model_name, model=model, enforce_detection = False, silent =True) as “model” [“VGG-Face”, “Facenet”, “Facenet512”, “OpenFace”, “DeepFace”, “DeepID”, “ArcFace”, “Dlib”, “SFace”] are used (I want to test them all for my usecase) and have some questions:

1.) When using enforce_detection = False, do the images in db_path should only contain the face? (I guess this must be true) 2.) The creation of my face-database is done in a different script (lets call it training.py), so, do I need to somehow (pre-)process the faces (for example with cv2.resize(face_image, (224, 224)) or normalize/standardize the faces in a certain way? 3.) If 2.) is not needed, should the stored faces have a minimum/maximum pixel-size? The face detection algorithm used can detect faces up to a few meters from the camera and some faces have like 20x40 pixels 4.) When DeepFace.find() is called the first time it creates a representations_deeface.pkl file with the embeddings of the faces in db_path (as far as I understand) and reuses the .pkl if it was already created. In my usecase the user can add persons/faces and the name to the database at will, is it possible to somehow “update” the .pkl? 5.) What is in your opinion the “best” “middle-ground” model for a live-analysis setting in terms of accuracy and speed? Due to this setting at least a few FPS is desirable. 6.) Does the choice of the distance_metric function has a significant impact on the recognized faces?

Thank you for your time!

[Terminazor]

@serengil Regarding 4): I have looked through the code and found the part where the embeddings are calculated and the .pkl file is stored. I put it all into a function and it seems to do the job. The only thing I added was the removal of the .pkl file right before saving it again with the new embeddings:

def update_database(db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv', align = True, prog_bar = True, normalization = 'base', silent=False):

	if os.path.isdir(db_path) == True:

		# Load or build model
		if model == None:

			if model_name == 'Ensemble':
				if not silent: print("Ensemble learning enabled")
				models = Boosting.loadModel()

			else: #model is not ensemble
				model = build_model(model_name)
				models = {}
				models[model_name] = model

		else: #model != None
			if not silent: print("Already built model is passed")

			if model_name == 'Ensemble':
				Boosting.validate_model(model)
				models = model.copy()
			else:
				models = {}
				models[model_name] = model

		#---------------------------------------

		if model_name == 'Ensemble':
			model_names = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
			metric_names = ['cosine', 'euclidean', 'euclidean_l2']
		elif model_name != 'Ensemble':
			model_names = []; metric_names = []
			model_names.append(model_name)
			metric_names.append(distance_metric)

		#---------------------------------------

		file_name = "representations_%s.pkl" % (model_name)
		file_name = file_name.replace("-", "_").lower()

		#find representations for db images
		employees = []
		representations = []
		pbar = tqdm(range(0,len(employees)), desc='Finding representations', disable = prog_bar)

		#for employee in employees:
		for index in pbar:
			employee = employees[index]

			instance = []
			instance.append(employee)

			for j in model_names:
				custom_model = models[j]

				representation = represent(img_path = employee
					, model_name = model_name, model = custom_model
					, enforce_detection = enforce_detection, detector_backend = detector_backend
					, align = align
					, normalization = normalization
					)

				instance.append(representation)

			#-------------------------------

			representations.append(instance)
			
		if path.exists(db_path+'/'+file_name):
			os.remove(db_path+'/'+file_name)

		f = open(db_path+'/'+file_name, "wb")
		pickle.dump(representations, f)
		f.close()

		if not silent: print("Representations stored in ",db_path,"/",file_name," file. This file has been updated!")
	
	else:
		raise ValueError("Passed db_path does not exist!")

[Risingabhi]

1- pass img_path to represent function directly

embeddings = represent(img_path = "C:/Users/Risin/Desktop/Students/Student/1002/1012/AdarshKumar_72_1.jpg"
, model_name='VGG-Face', enforce_detection= False, detector_backend='mtcnn')

2- do not use normalization

3- why cosine is weird? if it is less than 0.40, then they are same person. (Ref: https://github.com/serengil/deepface/blob/master/deepface/commons/distance.py)

Refer to point 3 by you: when i run this, at times i get different person, as Identified. I am extracting face images by using MTCNN() and then calculating its vector (VGG-Face) . check the verificiation result of first 5 images.