Giving multiple detected faces at once to recognition model

hi. I am able to detect faces in an image with scrfd. If there is more than one face in the image, I have to give the faces one by one to the Recognition model, respectively.
How can I do this in such a way that I get all the faces in one go (at once ) and get the features in less time?

```
bboxes, kpss = self.det_model.detect(img,
max_num=max_num,
metric='default')
if bboxes.shape[0] == 0:
return []
ret = []
for i in range(bboxes.shape[0]):

bbox = bboxes[i, 0:4]
det_score = bboxes[i, 4]
kps = None
if kpss is not None:
kps = kpss[i]
face = Face(bbox=bbox, kps=kps, det_score=det_score)

for taskname, model in self.models.items():

if taskname=='detection':
continue
model.get(img, face)
ret.append(face)


return ret
```

get feature :
```
def get_feat(self, imgs):
if not isinstance(imgs, list):
imgs = [imgs]

input_size = self.input_size

blob = cv2.dnn.blobFromImages(imgs, 1.0 / self.input_std, input_size,
(self.input_mean, self.input_mean, self.input_mean), swapRB=True)

net_out = self.session.run(self.output_names, {self.input_name: blob})[0]

return net_out
```