I'm searching about good feature extractor (library or pretrained architecture for pytorch), except dlib, for implementing face clustering with DBSCAN.

I tried openface pretrained features via opencv's dnn module by next code :

import cv2 
import numpy as np 
from pathlib import Path
from sklearn.cluster import DBSCAN
import multiprocessing
 
dataset_path = 'datasetpath'
embedder = cv2.dnn.readNetFromTorch('openface.nn4.small2.v1.t7')

face_list = []
vec_list = []

for i in Path(dataset_path).glob("*.jpg"):
    frame = cv2.imread(str(i))
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5, minSize=None, maxSize=None)
    for coords in faces:
        face = frame[coords[1]:coords[1]+coords[3], coords[0]:coords[0]+coords[2]]
        face = cv2.resize(face, (96, 96))
        faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255,
            (96, 96),
            (0, 0, 0), 
            swapRB=True,
            crop=False)
        embedder.setInput(faceBlob)
        vec = embedder.forward().flatten()
        face_list.append(face)
        vec_list.append(vec)


clt = DBSCAN(eps=0.5, metric="euclidean", min_samples=5, n_jobs=multiprocessing.cpu_count())
clt.fit_predict(vec_list)
clt.labels_

and results are :

array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1])

It is clear that DBSCAN failed to find no dense cluster, all faces clustered as just outlier. My dataset is same with the This post. I used same parameters on DBSCAN with post, so i doubt the openface feature is disperse or respond to a subtle difference on face, do not agregated enough to initial seed for DBSCAN. I changed eps and min_samples but the results are same. Is there anything that the good face feature extractor or pretrained models, that especially can be used with pytorch or cv2.dnn module?