diff --git a/client/models/face_recognition/recognition.py b/client/models/face_recognition/recognition.py
index 674e9b6..5632dba 100644
--- a/client/models/face_recognition/recognition.py
+++ b/client/models/face_recognition/recognition.py
@@ -1,15 +1,12 @@
-from importlib import resources
-
 import numpy as np
-from data.routines import FACES_FOLDER, register_face_embeddings, iter_face_embeddings
-from deepface import DeepFace
 import face_recognition
 
-MODEL_NAME = "GhostFaceNet"
-
+from data.routines import register_face_embeddings, iter_face_embeddings
 
-def _path_to_user_id(path: str) -> str:
-    return path.split("/")[-2]
+MODEL_NAME = "small"
+TOO_MANY_FACES = -2
+NO_MATCH = -1
+OK = 0
 
 
 def get_face_match(login_face: np.ndarray) -> int:
@@ -20,22 +17,13 @@ def get_face_match(login_face: np.ndarray) -> int:
         login_face: Image of user's face as an array
 
     Returns:
-        Matching user id, or -1 if no users matched
+        Matching user id, -1 if no users matched, -2 if too many faces
     """
-    # with resources.as_file(FACES_FOLDER) as faces_folder:
-    #     try:
-    #         dfs = DeepFace.find(login_face, str(faces_folder), model_name=MODEL_NAME)
-    #     except ValueError:
-    #         return -1
-    #     df = dfs[0]
-    #     user_id = int(_path_to_user_id(df.iloc[0]["identity"]))
-    #     return user_id
-
-    login_embeddings = face_recognition.face_encodings(login_face, model="small")
+    login_embeddings = face_recognition.face_encodings(login_face, model=MODEL_NAME)
 
     # Should only detect one face
     if len(login_embeddings) != 1:
-        return -1
+        return TOO_MANY_FACES
     login_embedding = login_embeddings[0]
 
     for user_id, user_embeddings in iter_face_embeddings():
@@ -44,21 +32,30 @@ def get_face_match(login_face: np.ndarray) -> int:
         if any(matches):
             return user_id
 
-    return -1
+    return NO_MATCH
 
 
 def register_faces(user_id: int, faces: list[np.ndarray]) -> int:
+    """Compute and store face embeddings in the database.
+
+    Args:
+        user_id: Id of the user who belongs to the faces
+        faces: List of face images in the shape HxWxC where (C)hannels are in RGB
+
+    Returns:
+        Status, -2 if too many faces in an image, 0 if registration was sucessful.
+    """
     face_embeddings = []
     for face in faces:
-        all_faces_embed = face_recognition.face_encodings(face, model="small")
+        all_faces_embed = face_recognition.face_encodings(face, model=MODEL_NAME)
 
         # Should only detect one face
         if len(all_faces_embed) != 1:
-            return -1
+            return TOO_MANY_FACES
 
         face_embedding = all_faces_embed[0]
         face_embeddings.append(face_embedding)
 
     register_face_embeddings(user_id, face_embeddings)
 
-    return 0
+    return OK