Here is an example of a liveness algorithm in Python using the Opencv and dlib libraries

Here is an example of a liveness algorithm in Python using the OpenCV and dlib libraries:

```
cv2 import

import dlib

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

cap=cv2.VideoCapture(0)

so far true:

ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector (gray, 0)
for face in faces:
landmarks = predictor(grey, face)
left_eye_ratio = eye_aspect_ratio(landmarks, 36, 37, 38, 39, 40, 41)
right_eye_ratio = eye_aspect_ratio(landmarks, 42, 43, 44, 45, 46, 47)
eye_avg_ratio = (left_eye_ratio + right_eye_ratio) / 2
if eye_avg_ratio < 0.25:
print("Liveness detection: eyes closed")
more:
print("Liveness detection: eyes open")
cv2.imshow("frame", frame)
if cv2.waitKey(1) == ord('q'):
break

cap.release()

cv2.destroyAllWindows()
```


This code captures frames from a webcam and uses the dlib library to detect faces and facial landmarks. The aspect ratio of the eye (EAR) is calculated based on the position of certain landmarks around the eyes. If the EAR is below a certain threshold (0.25 in this example), the program assumes that the eyes are closed and displays a message. If the EAR is above the threshold, the program assumes that the eyes are open.