diff --git a/src/cuda_t3.py b/src/cuda_t3.py
index 93438f8..2c95232 100644
--- a/src/cuda_t3.py
+++ b/src/cuda_t3.py
@@ -39,7 +39,7 @@ def comprehensive_gpu_test():
     cv2.rectangle(test_image, (100, 100), (200, 200), (255, 255, 255), -1)
     cv2.rectangle(test_image, (300, 150), (400, 250), (255, 255, 255), -1)
     cv2.rectangle(test_image, (500, 200), (600, 300), (255, 255, 255), -1)
-    cv2.imwrite("test_data/multi_face_test.jpg", test_image)
+    # cv2.imwrite("test_data/multi_face_test.jpg", test_image)
 
     print("✅ 测试数据创建完成")
 
diff --git a/src/video_face_recognition.py b/src/video_face_recognition.py
new file mode 100644
index 0000000..61729f7
--- /dev/null
+++ b/src/video_face_recognition.py
@@ -0,0 +1,340 @@
+# video_face_recognition.py
+import cv2
+import numpy as np
+import time
+from insightface.app import FaceAnalysis
+from typing import List, Dict, Tuple
+import os
+
+
+class VideoFaceRecognition:
+    """
+    视频人脸识别系统
+    支持实时视频流和视频文件处理
+    """
+
+    def __init__(self, model_name: str = 'buffalo_l', use_gpu: bool = True):
+        # 初始化人脸识别模型
+        self.app = FaceAnalysis(name=model_name)
+        self.app.prepare(
+            ctx_id=0 if use_gpu else -1,
+            det_thresh=0.3,
+            det_size=(640, 640)
+        )
+
+        self.target_embedding = None
+        self.target_id = None
+        self.similarity_threshold = 0.3
+
+        # 性能统计
+        self.frame_count = 0
+        self.processing_times = []
+
+        print(f"✅ 视频人脸识别系统初始化完成 - GPU: {use_gpu}")
+
+    def set_target_face(self, image_path: str, person_id: str = "target") -> bool:
+        """设置目标人脸"""
+        img = cv2.imread(image_path)
+        if img is None:
+            print(f"❌ 无法读取目标图像: {image_path}")
+            return False
+
+        faces = self.app.get(img)
+        if not faces:
+            print(f"❌ 目标图像中未检测到人脸: {image_path}")
+            return False
+
+        self.target_embedding = faces[0].embedding
+        self.target_id = person_id
+        print(f"✅ 目标人脸设置: {person_id}")
+        return True
+
+    def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict]]:
+        """
+        处理单帧图像
+        返回: (处理后的帧, 识别结果列表)
+        """
+        start_time = time.time()
+
+        # 人脸检测和识别
+        faces = self.app.get(frame)
+
+        results = []
+        for face in faces:
+            similarity = 0.0
+            if self.target_embedding is not None:
+                # 计算相似度
+                emb1 = face.embedding / np.linalg.norm(face.embedding)
+                emb2 = self.target_embedding / np.linalg.norm(self.target_embedding)
+                similarity = float(np.dot(emb1, emb2))
+
+            result = {
+                'bbox': face.bbox.astype(int).tolist(),
+                'similarity': similarity,
+                'is_match': similarity >= self.similarity_threshold,
+                'gender': 'Male' if face.gender == 1 else 'Female',
+                'age': int(face.age),
+                'det_score': float(face.det_score)
+            }
+            results.append(result)
+
+            # 在帧上绘制结果
+            frame = self._draw_detection(frame, result)
+
+        # 性能统计
+        processing_time = (time.time() - start_time) * 1000
+        self.processing_times.append(processing_time)
+        self.frame_count += 1
+
+        return frame, results
+
+    def _draw_detection(self, frame: np.ndarray, result: Dict) -> np.ndarray:
+        """在帧上绘制检测结果"""
+        bbox = result['bbox']
+        similarity = result['similarity']
+        is_match = result['is_match']
+
+        # 选择颜色
+        if is_match:
+            color = (0, 255, 0)  # 绿色 - 匹配
+        # elif similarity > self.similarity_threshold/2:
+        #     color = (0, 255, 255)  # 黄色 - 中等相似度
+        else:
+            color = (0, 0, 255)  # 红色 - 不匹配
+
+        # 绘制人脸框
+        x1, y1, x2, y2 = bbox
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+
+        # 绘制信息文本
+        if self.target_id:
+            status = f"MATCH: {similarity:.3f}" if is_match else f"NO MATCH: {similarity:.3f}"
+            # text = f"{self.target_id}: {status}"
+            text = status
+        else:
+            text = f"Similarity: {similarity:.3f}"
+
+        # 文本背景
+        text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
+        cv2.rectangle(frame, (x1, y1 - text_size[1] - 10), (x1 + text_size[0], y1), color, -1)
+
+        # 文本
+        cv2.putText(frame, text, (x1, y1 - 5),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+
+        # 详细信息
+        info_text = f"{result['gender']}/{result['age']}"
+        cv2.putText(frame, info_text, (x1, y2 + 20),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+        return frame
+
+    def process_video_file(self, video_path: str, output_path: str = None,
+                           skip_frames: int = 0, show_preview: bool = True):
+        """
+        处理视频文件
+
+        Args:
+            video_path: 输入视频路径
+            output_path: 输出视频路径
+            skip_frames: 跳帧数，用于提高处理速度
+            show_preview: 是否显示实时预览
+        """
+        if not os.path.exists(video_path):
+            print(f"❌ 视频文件不存在: {video_path}")
+            return
+
+        # 打开视频文件
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print(f"❌ 无法打开视频文件: {video_path}")
+            return
+
+        # 获取视频信息
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+        print(f"📹 视频信息: {width}x{height}, {fps:.1f}FPS, 总帧数: {total_frames}")
+
+        # 设置输出视频
+        if output_path:
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            out = cv2.VideoWriter(output_path, fourcc, fps/(skip_frames), (width, height))
+        else:
+            out = None
+
+        # 处理视频帧
+        frame_index = 0
+        processed_frames = 0
+        start_time = time.time()
+
+        print("🚀 开始处理视频...")
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # 跳帧处理
+            if skip_frames > 0 and frame_index % (skip_frames + 1) != 0:
+                frame_index += 1
+                continue
+
+            # 处理当前帧
+            processed_frame, results = self.process_frame(frame)
+
+            # 写入输出视频
+            if out:
+                out.write(processed_frame)
+
+            # 显示预览
+            if show_preview:
+                # 添加性能信息
+                fps_text = f"Frame: {frame_index}/{total_frames} | Faces: {len(results)}"
+                cv2.putText(processed_frame, fps_text, (10, 30),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
+
+                cv2.imshow('Video Face Recognition', processed_frame)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+
+            frame_index += 1
+            processed_frames += 1
+
+            # 进度显示
+            if frame_index % 30 == 0:
+                progress = (frame_index / total_frames) * 100
+                print(f"📊 处理进度: {progress:.1f}% ({frame_index}/{total_frames})")
+
+        # 清理资源
+        cap.release()
+        if out:
+            out.release()
+        if show_preview:
+            cv2.destroyAllWindows()
+
+        # 性能统计
+        total_time = time.time() - start_time
+        avg_processing_time = np.mean(self.processing_times) if self.processing_times else 0
+
+        print(f"\n🎉 视频处理完成!")
+        print(f"📊 性能统计:")
+        print(f"   总处理帧数: {processed_frames}")
+        print(f"   总耗时: {total_time:.1f}秒")
+        print(f"   平均每帧: {avg_processing_time:.1f}ms")
+        print(f"   实际FPS: {processed_frames / total_time:.1f}")
+        if output_path:
+            print(f"   输出视频: {output_path}")
+
+    def process_webcam(self, camera_id: int = 0, output_path: str = None):
+        """
+        处理摄像头实时视频流
+        """
+        cap = cv2.VideoCapture(camera_id)
+        if not cap.isOpened():
+            print(f"❌ 无法打开摄像头 {camera_id}")
+            return
+
+        # 设置摄像头分辨率（可选）
+        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
+        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
+
+        # 设置输出视频
+        if output_path:
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        else:
+            out = None
+
+        print("🎥 开始摄像头实时识别 (按 'q' 退出)...")
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                print("❌ 无法读取摄像头帧")
+                break
+
+            # 处理当前帧
+            processed_frame, results = self.process_frame(frame)
+
+            # 添加实时信息
+            current_fps = 1000 / self.processing_times[-1] if self.processing_times else 0
+            info_text = f"FPS: {current_fps:.1f} | Faces: {len(results)}"
+            cv2.putText(processed_frame, info_text, (10, 30),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
+
+            # 写入输出
+            if out:
+                out.write(processed_frame)
+
+            # 显示预览
+            cv2.imshow('Real-time Face Recognition', processed_frame)
+
+            # 按'q'退出
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+        # 清理资源
+        cap.release()
+        if out:
+            out.release()
+        cv2.destroyAllWindows()
+
+        print("✅ 摄像头处理结束")
+
+
+# 使用示例
+def main():
+    # 创建视频识别系统
+    video_system = VideoFaceRecognition(use_gpu=True)
+
+    # 设置目标人脸（可选）
+    target_image = "test_data/register/person1.png"
+    # target_image = "test_data/register/cjh.jpg"
+
+    if os.path.exists(target_image):
+        video_system.set_target_face(target_image, "目标人物")
+
+    # 选择处理模式
+    print("请选择处理模式:")
+    # print("1. 处理视频文件")
+    # print("2. 实时摄像头")
+
+    # choice = input("请输入选择 (1 或 2): ").strip()
+    choice = "1";
+
+    if choice == "1":
+        # 处理视频文件
+        video_path = "test_data/video/test_video.mp4"
+        output_path = "test_data/output_video/zqc.mp4"
+
+        # 性能优化：跳帧处理
+        skip_frames = 1  # 每2帧处理1帧，提高速度
+
+        video_system.process_video_file(
+            video_path=video_path,
+            output_path=output_path,
+            skip_frames=skip_frames,
+            show_preview=True
+        )
+
+    elif choice == "2":
+        # 实时摄像头
+        output_path = "webcam_recording.mp4"  # 可选：保存录制
+
+        video_system.process_webcam(
+            camera_id=0,
+            output_path=output_path
+        )
+
+    else:
+        print("❌ 无效选择")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file