deepface for generating embedding for multiple faces in single image - deepface

Question

i am using deep face library for the face recognition project. in my case, I want to detect multiple faces present in the test image using facenet. when I apply deepface preprocess function i see that it generates only one embedding where as in the given image four faces are present. how can i get respective embeddings for each face?
**my code looks:**
import deepface as DeepFace
from elasticsearch import Elasticsearch
from deepface.basemodels import Facenet
import os
from deepface.commons import functions
model = Facenet.loadModel()
target_size = (160, 160)
embedding_size = 128
backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']
target_path = "/home/niveus/PycharmProjects/deepface-elastic-research/deepface/align_img/deep_aku.jpg"
target_img = functions.preprocess_face(target_path, target_size = target_size,detector_backend = backends[3])
target_embedding = model.predict(target_img)[0] #[0]
print(target_embedding.shape)
print("embeddings",target_embedding)

Answer 1

Deepface enforces you to use single faces in an image. But you are still able to process multiple faces.
1- Extract faces with retinaface
#!pip install retina-face
from retinaface import RetinaFace
faces = RetinaFace.extract_faces(img_path = "img.jpg", align = True)
2- Pass extracted faces to deepface
#!pip install deepface
from deepface import DeepFace
embeddings = []
for face in faces:
embedding = DeepFace.represent(img_path = face, model_name = 'Facenet', enforce_detection = False)
embeddings.append(embedding)
The trick is to set the enforce detection argument to false because we will pass already detected faces to deepface.

Answer 2

You are also able to use MTCNN instead of RetinaFace. RetinaFace is amazing but it is very slow. Instead, mtcnn comes with high performance and speed.
from mtcnn import MTCNN
from deepface import DeepFace
import cv2
img = cv2.cvtColor(cv2.imread("deepface/tests/dataset/img1.jpg"), cv2.COLOR_BGR2RGB)
detector = MTCNN()
detections = detector.detect_faces(img)
embeddings = []
for detection in detections:
confidence = detection["confidence"]
if confidence > 0.90:
x, y, w, h = detection["box"]
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
embedding = DeepFace.represent(detected_face, model_name = 'Facenet', enforce_detection = False)
embeddings.append(embedding)
The both retinaface and mtcnn find facial landmarks such as eyes. In that way, deepface can align faces in the background and it increases the quality of embeddings dramatically. However, opencv is not good at finding landmarks. That's why, if you are going to use opencv, its score might be low because of the alignment.