diff --git a/.gitignore b/.gitignore
index f0627d98ed0d87a41ecc53b1373a5c64d81b9652..86babfcf7b0fd59a61e897d5f9d03d3983ee0c49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -104,5 +104,8 @@ ENV/
 .idea/
 logs/
 20170512-110547/
+20180402-114759/
+20180408-102900/
 
-*.sqlite
\ No newline at end of file
+*.sqlite
+*.zip
\ No newline at end of file
diff --git a/app/api/users.py b/app/api/users.py
index 22b38844a127a528e5c518179f508b819fafb934..e7f367be84faf1f2ec4656871f2f8639b476141e 100644
--- a/app/api/users.py
+++ b/app/api/users.py
@@ -65,8 +65,10 @@ def face_recognition():
     x = np.array(x)
     y = np.array(y)
     LOG.info([embedding.shape, x.shape, y.shape])
-    #    results = compare.face_net_compare(embedding, x, y)
-    #    user = User.query.filter_by(id=int(results[0])).first()
-    results = compare.face_net_compare(embedding, x, y)
-    user = User.query.filter_by(id=int(results)).first()
+    target = compare.face_net_compare(embedding, x, y)
+    if target == 0:
+        user = User()
+        user.from_dict({'id': 0, 'name': '无法识别', 'cell_phone_number': ''})
+    else:
+        user = User.query.filter_by(id=int(target)).first()
     return jsonify(success=True, data=user.to_dict(), status_code=200)
diff --git a/app/recognition/compare.py b/app/recognition/compare.py
index 57f0d62247b7f34d7970466de93bea8d6d3d0079..44c038ed3ca91b581ae2a91a2b47ddee110d2526 100644
--- a/app/recognition/compare.py
+++ b/app/recognition/compare.py
@@ -1,30 +1,24 @@
-import numpy as np
-'''from sklearn.neighbors import KNeighborsClassifier
+"""from sklearn.neighbors import KNeighborsClassifier
 
 
 def face_net_compare(x, X, y, n=3):
     knn_classifier = KNeighborsClassifier(n_neighbors=n)
     knn_classifier.fit(X, y)
-    return knn_classifier.predict(x)'''
-
-def fac_net_compare(embeddings, x, y, threshold = 1.0):
-    mindis_id = 0;
-    cnt = 0;
-    list_dis = [];
-    for emb in embeddings:
-        dis = np.sqrt(np.sum(np.squre(np.subtract(emb, x))));
-        list_dis.append(dis)
-        if(dis < list_dis[mindis_id]):
-            mindis_id = cnt;
-        ++cnt;
-    mindis = list_dis[mindis_id];
-    if(mindis > threshold):
-        return y[mindis_id];
-    else:
-        return y[mindis_id];
-
-
-
+    return knn_classifier.predict(x)"""
 
+import numpy as np
 
 
+def face_net_compare(embedding, x, y, threshold=0.5):
+    distances = []
+    for i in range(len(x)):
+        distance = np.sqrt(np.sum(np.square(np.subtract(embedding, x[i]))))
+        distances.append(distance)
+    min_distance_id = np.argmin(distances)
+    assert isinstance(min_distance_id, np.int64), 'Type of min_distance_id is not int.'
+    min_distance = distances[min_distance_id]
+    print("distance = %.8f | minimum distance id = %d" % (min_distance, min_distance_id))
+    if min_distance > threshold:
+        return 0
+    else:
+        return y[min_distance_id]
diff --git a/tests/test_face_net.py b/tests/test_face_net.py
index ef8b42dc37bf73c4d43b6c11d80714523daca885..f847a46e1f0568c248220998d053aaae16795654 100644
--- a/tests/test_face_net.py
+++ b/tests/test_face_net.py
@@ -2,6 +2,7 @@ import unittest
 from pathlib import Path
 
 import numpy as np
+import tensorflow.compat.v1 as tf
 
 from app.recognition.face_net import FaceNet
 from app.utils import file_utils
@@ -13,8 +14,20 @@ MODEL_PATH_PREFIX = Path(__file__).parents[1]
 
 class FaceNetTestCase(unittest.TestCase):
 
+    def test_model_node(self):
+        model_path = str(MODEL_PATH_PREFIX / '20180402-114759' / '20180402-114759.pb')
+        with tf.gfile.FastGFile(model_path, "rb") as f:
+            graph_def = tf.GraphDef()
+            graph_def.ParseFromString(f.read())
+            tf.import_graph_def(graph_def)
+        graph = tf.get_default_graph()
+        operations = graph.get_operations()
+        self.assertTrue(len(operations) > 0)
+        for operation in operations:
+            print(operation.name)
+
     def test_model_exists(self):
-        model_path = MODEL_PATH_PREFIX / '20170512-110547' / '20170512-110547.pb'
+        model_path = MODEL_PATH_PREFIX / '20180402-114759' / '20180402-114759.pb'
         print(model_path)
         self.assertTrue(model_path.exists())