diff --git a/api/routes/algorithm_router.py b/api/routes/algorithm_router.py
index e6cbba0..3698123 100644
--- a/api/routes/algorithm_router.py
+++ b/api/routes/algorithm_router.py
@@ -745,7 +745,7 @@ def sync_videofacebiz_blacklist():
                         feature_array = np.frombuffer(feature.feature_data, dtype=np.float32)
                         
                         # 使用person_id作为标识符
-                        person_name = f"blacklist_{feature.person_id}"
+                        person_name = f"{feature.person_id}"
                         registered_faces[person_name] = feature_array
                         loaded_count += 1
                         
@@ -970,7 +970,7 @@ def sync_videofaceprisonbiz_blacklist():
                         feature_array = np.frombuffer(feature.feature_data, dtype=np.float32)
                         
                         # 使用person_id作为标识符
-                        person_name = f"blacklist_{feature.person_id}"
+                        person_name = f"{feature.person_id}"
                         registered_faces[person_name] = feature_array
                         loaded_count += 1
                         
diff --git a/biz/video_face_prison_biz.py b/biz/video_face_prison_biz.py
index bc345c6..9746287 100644
--- a/biz/video_face_prison_biz.py
+++ b/biz/video_face_prison_biz.py
@@ -5,7 +5,7 @@
 
 import cv2
 import numpy as np
-from typing import Optional, List, Dict
+from typing import Optional, List, Dict, Tuple
 import time
 from insightface.app import FaceAnalysis
 from biz.base_face_biz import BaseFaceBiz
@@ -64,4 +64,39 @@ class VideoFacePrisonBiz(BaseFaceBiz):
 
         return frame
 
+    def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict], float]:
+        """
+        处理单帧图像
+
+        返回:
+            (原始帧, 识别结果列表, 处理时间ms)
+        """
+        start_time = time.time()
+
+        # 人脸检测和识别
+        faces = self.app.get(frame)
+
+        results = []
+        for face in faces:
+            # 检查人脸质量是否可接受
+            is_acceptable, quality_metrics = self.is_face_quality_acceptable(face, frame)
+
+            # 查找最佳匹配
+            best_name, similarity = self.find_best_match(face.embedding)
+
+            is_match = best_name is not None and similarity >= self.similarity_threshold
+
+            result = {
+                'bbox': face.bbox.astype(int).tolist(),
+                'similarity': similarity,
+                'best_match': best_name,
+                'is_match': is_match,
+                'det_score': float(face.det_score),
+                'quality_metrics': quality_metrics,
+                'is_acceptable': is_acceptable
+            }
+            results.append(result)
+
+        processing_time = (time.time() - start_time) * 1000
+        return frame, results, processing_time