diff --git a/person_manager.py b/person_manager.py
index 5e54896..f43769f 100644
--- a/person_manager.py
+++ b/person_manager.py
@@ -537,7 +537,7 @@ class PersonManager:
                 if save_new_person and confirmed_change:
                     for person in identified_persons:
                         if person['is_new'] and len(self.persons) < self.config['max_persons']:
-                            # 保存人脸特征
+                            # 保存人脸特征和人脸图片
                             x, y, w, h = person['bbox']
                             face_region = image[y:y+int(h*0.4), x:x+w]
                             
@@ -548,8 +548,8 @@ class PersonManager:
                                     person['person_id'] = person_id
                                     person['name'] = f"Person #{len(self.persons) + 1}"
                                     
-                                    # 保存到人员库
-                                    self.add_new_person_with_encoding(person_id, encoding, person['name'])
+                                    # 保存到人员库（包括人脸图片）
+                                    self.add_new_person_with_encoding(person_id, encoding, person['name'], face_region)
                     
                 # 清空缓冲区，更新状态
                 self.confirmation_buffer = {key: self.confirmation_buffer[key]}
@@ -580,13 +580,14 @@ class PersonManager:
             'detection_source': 'yolo'
         }
     
-    def add_new_person_with_encoding(self, person_id, encoding, name=None):
+    def add_new_person_with_encoding(self, person_id, encoding, name=None, face_image=None):
         """保存新人员到库（已有 encoding）
         
         Args:
             person_id: 人员ID
             encoding: 特征向量
             name: 名称
+            face_image: 人脸图片（可选，用于保存人脸图片）
             
         Returns:
             dict: 人员信息
@@ -594,10 +595,18 @@ class PersonManager:
         if name is None:
             name = person_id
         
+        # 保存人脸图片
+        face_path = ''
+        if face_image is not None and face_image.size > 0:
+            face_path = str(self.faces_dir / f"{person_id}.jpg")
+            cv2.imwrite(face_path, face_image)
+            print(f"[PersonManager] Face image saved: {face_path}")
+        
         person_data = {
             'person_id': person_id,
             'name': name,
             'face_encoding': encoding.tolist() if isinstance(encoding, np.ndarray) else encoding,
+            'face_path': face_path,
             'first_seen': datetime.datetime.now().isoformat(),
             'last_seen': datetime.datetime.now().isoformat(),
             'visit_count': 1