diff --git a/sbin/ctl.sh b/sbin/ctl.sh
index 262714422..97f000b60 100755
--- a/sbin/ctl.sh
+++ b/sbin/ctl.sh
@@ -27,6 +27,7 @@
 #
 # sudo pip3 install openvino opencv-python yolov5 yolov8
 
+. awesome_venv/bin/activate
 
 HERE=$PWD
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
diff --git a/src/deepfacex.py b/src/deepfacex.py
index 26e326550..cded3a5b7 100644
--- a/src/deepfacex.py
+++ b/src/deepfacex.py
@@ -1,50 +1,179 @@
+import sys 
+import json
+import os
+# Generic deepface
 from deepface import DeepFace
+from deepface.detectors import FaceDetector
+# dlib face detector
+import dlib
+# facenet512
+from keras.models import model_from_json
+from faceclass import FaceClass
+#from inception_resnet_v1 import *
 
-class Deepfacex(object):
+#
+# Use "deepface" to perform face matching.
+# The first time it runs it will download a LOT of stuff.
+#  metrics = ["cosine", "euclidean", "euclidean_l2"]
+#  backends = [ 'opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe', 'yolov8', 'yunet', 'fastmtcnn'
+#  models = [ "VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace" ]
+
+
+class Deepfacex(FaceClass):
 
     img1_path = ""
     img2_path = ""
-    modelname = "facenet512"
+    backend = "ssd"
+    imgs = {}
+    faces = {}
+    fn512_model = None
+    resnet_model = None
+    visual = 0
+     
+    def init(self, backend, model):
+        print("Loading models...")
+        self.backend = backend
+        self.model = model
+        #self.fn512_model = model_from_json(open("modules/facenet512/facenet_model.json", "r").read())
+        #self.fn512_model.load_weights('modules/facenet512/facenet_weights.h5')
+        #self.resnet_model = InceptionResNetV1()
+        #model.summary()
+        print("Loaded models ;)")
 
-    # Load the pics and device labels
-    def load(self, dev1, dev2, id_image_filepath, photo_image_filepath):
-        print("load1")
-        self.dev1 = dev1
-        self.dev2 = dev2
-        try:
-            # Load images
-            print("load2")
-            self.img1_path = load_image(id_image_filepath)
-            self.img2_path = load_image(photo_image_filepath)
-            print("++++++++++++++++ ",self.id_image)
-            return True
-        except:
-            return None
+    def clear(self):
+        self.imgs = {}
+        self.faces = {}
+
+    def l2_normalize(x):
+        return x / np.sqrt(np.sum(np.multiply(x, x)))
+
+
+    def dlib_detector(self, name):
+        UPSAMPLE = 1
+        detector = dlib.get_frontal_face_detector()
+        #img = dlib.load_rgb_image(fname)
+        dets = detector(self.imgs[name], UPSAMPLE)
+        print("Number of faces detected in %s: %d" % (name, len(dets)))
+        for i, d in enumerate(dets):
+            print("Detection %d: Left: %d Top: %d Right: %d Bottom: %d" % (i, d.left(), d.top(), d.right(), d.bottom() ) )
+
+
+    def dlib_detector2(self, name):
+        UPSAMPLE = 1
+        detector = dlib.get_frontal_face_detector()
+        #img = dlib.load_rgb_image(fname)
+        dets, scores, idx = detector.run(self.imgs[name], UPSAMPLE, -1)
+        print("Number of faces detected: %d" % len(dets))
+        for i, d in enumerate(dets):
+            print("Detection %d: Score: %d Type: %d Left: %d Top: %d Right: %d Bottom: %d" % (i, scores[i], idx[i], d.left(), d.top(), d.right(), d.bottom() ) )
+
+
+    # Load two pics using their device labels
+    def loads(self, dev1, dev2, id_image_filepath, photo_image_filepath):
+        self.load(dev1,id_image_filepath)
+        self.load(dev2,photo_image_filepath)
+
+
+    # Detects all the faces 
+    def detect_all(self, name):
+        detector = FaceDetector.build_model(self.backend) #set opencv, ssd, dlib, mtcnn or retinaface
+        self.faces[name] = FaceDetector.detect_faces(detector, self.backend, self.imgs[name])
+        print(" Found %d faces for %s" %  (len(self.faces[name]), name))
+        return len(self.faces[name])
 
 
     # Compare the two pics
-    def process(self):
-        print("proc1")
-        verification = DeepFace.verify(img1_path = self.img1_filepath, img2_path = self.img2_filepath, model_name=self.modelname, detector_backend="opencv", distance_metric="euclidian", enforce_detection=True, align=True, normalization="base")
-        print("proc2")
-        # Fields:
-        #  boolean verified; float distance; float max_threshold_to_verify; string model; string similarity_metric
+    def process(self,impath1,impath2):
+        print("Matching %s vs %s" % (impath1,impath2))
+        verification = DeepFace.verify(img1_path = impath1, img2_path = impath2, model_name=self.model,\
+         detector_backend=self.backend, distance_metric="euclidean", enforce_detection=False, align=True, normalization="base")
         return json.dumps(verification)
 
 
     def analyse(self):
-        analysis = DeepFace.analyze(img_path = self.img1_filepath, actions = ["age", "gender", "emotion", "race"]) 
+        analysis = DeepFace.analyze(img_path = self.img1_path, actions = ["age", "gender", "emotion", "race"]) 
         return json.dumps(analysis)
 
 
 
 if __name__ == '__main__':
-    print("main1")
+    # tests
+    #             big/clear      big/clear      small/side    small/clear   obscured       ig card         IR cam       some rotated
+    picfiles = [ "gerrard.jpg", "ronaldo.jpg", "messi1.jpg", "messi2.jpg", "ox_matt.png", "ox_govid.jpg", "cg_ir.jpg", "fivepeople.jpg" ]
 
+    # Test the dlib image detector
     d = Deepfacex()
-    print("main2")
-    d.load("file1","file2","testimg/test1.jpg","testimg/test2.jpg")
-    print("main3")
+    d.init("dlib","Facenet512")
 
-    print (d.process())
+    # quick test
+    if sys.argv[1]=="quick":
+        d.visual = 1
+        d.load("messi1","messi2","testimg/messi1.jpg","testimg/messi2.jpg")
+        sys.exit(0)
 
+    # 99.4% detection
+    if sys.argv[1]=="detect":
+        # lfw
+        n=0
+        print("LFW Testing")
+        for lfwdir in sorted(os.listdir("/home/carl/Downloads/lfw")):
+            for lfw in sorted(os.listdir("/home/carl/Downloads/lfw/" + lfwdir)):
+                d.load(lfw, "/home/carl/Downloads/lfw/" + lfwdir + "/" + lfw)
+                d.dlib_detector(lfw)
+                d.clear()
+                n+=1
+                if n > 100:
+                    sys.exit(0)
+
+
+    # Three difficult faces; retinaface,yolov8 find all 3;  dlib,mtcnn, finds 2;   opencv,ssd,mediapipe,yunet find <=1
+    if sys.argv[1]=="algo1a":
+        mod = 'Dlib'
+        d.load("ox_matt","testimg/ox_matt.png")
+        for back in [ 'opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe', 'yolov8', 'yunet', 'fastmtcnn' ]:
+            d.init(back,mod)
+            print (back,mod,d.detect_all("ox_matt"))
+        sys.exit(0)
+    # Five clear but rotated faces; dlib,mtcnn,retinaface,yolov8 find all 5;  ssd find 4;  opencv,mediapipe,yunet find <=3
+    if sys.argv[1]=="algo1b":
+        mod = 'Dlib'
+        d.load("5people","testimg/fivepeople.jpg")
+        for back in [ 'opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe', 'yolov8', 'yunet', 'fastmtcnn' ]:
+            d.init(back,mod)
+            print (back,mod,d.detect_all("5people"))
+        sys.exit(0)
+
+
+    # Try and find difficult messi matches; 
+    # Best performers: Dlib, Sface
+    if sys.argv[1]=="algo2a":
+        back = "yolov8"
+        for mod in [ "VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace" ]:
+            d.init(back,mod)
+            print (d.process("testimg/messi1.jpg", "testimg/messi2.jpg"))
+        sys.exit(0)
+    # Try and find more difficult matches; 
+    # Best performers: VGG(6/7), Facenet512(5/7), Dlib(6/7), Sface(7/7)
+    if sys.argv[1]=="algo2b":
+        difficult = [ "Abdullatif_Sener", "Adam_Scott", "Alex_Barros", "Alison_Lohman", "Amelie_Mauresmo", "Andy_Roddick", "Anna_Nicole_Smith" ]
+        back = "yolov8"
+        for dif in difficult:
+            for mod in [ "VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace" ]:
+                d.init(back,mod)
+                print (d.process("/home/carl/Downloads/lfw/0001/%s.jpg" % dif, "/home/carl/Downloads/lfw/0002/%s.jpg" % dif, ))
+        sys.exit(0)
+
+
+    # Get stats on a particular algorithm. dlib+DLib=99/100|971/1000=97.1%; dlib+Facenet512=94/100; dlib+VGG=87100; 
+    #  dlib+SFace=100/100|199/200|991/1000==99.1; 1662/1680=98.9%
+    if sys.argv[1]=="stats":
+        # lfw
+        n=0
+        print("LFW Stats")
+        d.init("dlib","SFace")
+        for lfw in sorted(os.listdir("/home/carl/Downloads/lfw/0002")):
+            print (d.process("/home/carl/Downloads/lfw/0001/" + lfw, "/home/carl/Downloads/lfw/0002/" + lfw))
+            d.clear()
+            n+=1
+            if n > 4000:
+                sys.exit(0)
\ No newline at end of file
diff --git a/src/yoloserv.py b/src/yoloserv.py
index f4e7c0fe7..ae40e359b 100644
--- a/src/yoloserv.py
+++ b/src/yoloserv.py
@@ -59,6 +59,16 @@ class yoloserv(object):
             from para_facematch import Facematch
             self.facematcher = Facematch()
             self.facematcher.init()
+        if "deepface" in self.devices:
+            print("Loading deepface facematch...")
+            from deepfacex import Deepfacex
+            self.facematcher = Deepfacex()
+            self.facematcher.init()
+        if "face_recognition" in self.devices:
+            print("Loading deepface facematch...")
+            from face_recognition import FaceRecognition
+            self.facematcher = FaceRecognition()
+            self.facematcher.init()
 
         # Image acquisition
         if "camera" in self.devices:
@@ -135,13 +145,16 @@ class yoloserv(object):
 
 
     # Find faces - the algorithm used depends on the device list and what actual file was loaded as self.camera
+
+    # Simply load an image
     @cherrypy.expose
     def svc_load_face(self,infile):
         self.facematcher.load(infile)
 
     @cherrypy.expose
     def svc_detect_faces(self,infile):
-        self.facematcher.detect_all(infile)
+        nfaces = self.facematcher.detect_all(self.imgdir + infile)
+        return '{ "status":0, "remark":"found faces", "count":%d }' % (nfaces) 
 
     # Find the most prominent object
     @cherrypy.expose
@@ -183,51 +196,21 @@ class yoloserv(object):
         if status is not None:
             return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
 
-        status = self.facematcher.get_faces()
+        status = self.facematcher.analyse()
         if status is not None:
-            return '{ "status":777245, "remark":"face finding failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+            return '{ "status":777242, "remark":"face loading failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
 
-        status = self.facematcher.compute_scores()
-        if status is not None:
-            return '{ "status":777243, "remark":"face scoring failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+        #status = self.facematcher.get_faces()
+        #if status is not None:
+        #    return '{ "status":777245, "remark":"face finding failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
 
-        jsonstr = self.facematcher.get_scores()
+        #status = self.facematcher.compute_scores()
+        #if status is not None:
+        #    return '{ "status":777243, "remark":"face scoring failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
+
+        jsonstr = self.facematcher.process()
         return '{ "status":0, "remark":"OK", "data":%s }' % (jsonstr)
 
-  ################
-  
-        if "facematch" in self.device_list and self.conf["emulate_facematch"]==0:
-            from facematch import Facematch
-            self.devices["facematch"] = Facematch()
-            
-
-    @cherrypy.expose
-    def facematch(self,dev1,dev2):
-        if self.conf["emulate_facematch"]:
-            return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"match_score":600} }' % (dev1,dev2)
-
-        if dev1 == "regula":
-            img1 = "/tmp/regula/Portrait_0.jpg"
-        if dev1 == "localcam":
-            img1 = "/tmp/localcam.png"
-        if dev2 == "regula":
-            img2 = "/tmp/regula/Portrait_0.jpg"
-        if dev2 == "localcam":
-            img2 = "/tmp/localcam.png"
-        if self.conf["emulate_facematch"]:
-            return '{ "status":0, "remark":"OK", "data":{} }'
-
-        self.devices["facematch"].load(dev1, dev2, img1, img2)
-        status = self.devices["facematch"].get_faces()
-        if status is not None:
-            return '{ "status":777242, "remark":"face matching failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
-
-        status = self.devices["facematch"].compute_scores()
-        if status is not None:
-            return '{ "status":777243, "remark":"face scoring failed", "guilty_param":"facematch", "guilty_value":"%s" }' % (status)
-
-        jsonstr = self.devices["facematch"].get_scores()
-        return '{ "status":0, "remark":"OK", "data":%s }' % (jsonstr)
 
 
     @cherrypy.expose