diff --git a/TrackingTool/tracking.py b/TrackingTool/tracking.py
index 935f324..79f9131 100644
--- a/TrackingTool/tracking.py
+++ b/TrackingTool/tracking.py
@@ -9,6 +9,7 @@ import requests
 import face_recognition
 import numpy as np
 import pickle
+import time
 # Khởi tạo danh sách rỗng để lưu trữ thông tin người dùng
 user_data = []
 history = []
@@ -17,6 +18,7 @@ screen_height = 1100
 WINDOW_QR_CODE = "QR Code"
 WINDOW_TRACKING = "Tracking"
 WINDOW_HISTORY = "History"
+# URL_API = "http://localhost:8000/api/v1"
 URL_API = "https://ms.prology.net/api/v1"
 data = [0]
 # Hàm thông báo bằng giọng nói
@@ -262,7 +264,10 @@ def encodeImgs(save_path="../DetectFace/encodings.pkl"):
 encodeListKnow = encodeImgs()
 print("Load data success")
 # Khởi tạo camera
+
 def main():
+    recognized_faces = {}
+    name_history = {}
     cap = cv.VideoCapture(0)
     face_cascade = cv.CascadeClassifier(cv.data.haarcascades + 'haarcascade_frontalface_default.xml')
     cv.namedWindow(WINDOW_TRACKING, cv.WINDOW_NORMAL)
@@ -278,22 +283,37 @@ def main():
         faceCurFrame = face_recognition.face_locations(frameS, model='hog')
         encodeCurFrame = face_recognition.face_encodings(frameS)
         frame = process_qr_code(frame)
+        current_time = time.time()
         for encodeFace, faceLoc in zip(encodeCurFrame, faceCurFrame):
             matches = face_recognition.compare_faces(encodeListKnow, encodeFace)
             faceDis = face_recognition.face_distance(encodeListKnow, encodeFace)
             # print(faceDis)
             matchIndex = np.argmin(faceDis)
 
-            if faceDis[matchIndex] < 0.3:
+            if faceDis[matchIndex] < 0.35:
                 name = classNames[matchIndex].upper()
-                process_face_detect(f"{name}\n{"Staff"}\n\n", frame)
+                 # If the face is recognized, track the timestamp
+                if name not in recognized_faces:
+                    recognized_faces[name] = current_time  # Store first detection time
+                else:
+                    elapsed_time = current_time - recognized_faces[name]
+                    if (name not in name_history) or (current_time - name_history[name] >= 60):
+                        if elapsed_time >= 2.5:  # If face is seen for 2s, execute script
+                            process_face_detect(f"{name}\n{"Staff"}\n\n", frame)
+                            name_history[name] = time.time()
+                            del recognized_faces[name]
+                        else:
+                            display_text(frame, f"Checking: "+str(round((elapsed_time/2.5)*100,2))+"%", (700, 55), 1, (0, 255, 255), 2)
+                    else:
+                        display_text(frame, f"Checked. Try after {round(60-(current_time - name_history[name]),0)}s", (600, 55), 1, (0, 255, 255), 2)
             else:
                 name = "Unknow"
+                recognized_faces = {}
                 display_text(frame, f"Face not found - use QRcode", (20, 55), 0.7, (6, 6, 255), 2)
             y1, x2, y2, x1 = faceLoc
             y1, x2, y2, x1 = y1*2, x2*2, y2*2, x1*2
             cv.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
-            cv.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%) - CHECKED", (20, 25), cv.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 2)
+            cv.putText(frame, name + f"({(1 - round(faceDis[matchIndex], 2))*100}%)", (x1, y1), cv.FONT_HERSHEY_COMPLEX, 0.8, (0,255,0), 2)