Liveness Detection Linux SDK
Face Liveness Detection Linux SDK
Code
Setup
Build docker image
sudo docker build -t face-liveness --progress=plain .Run docker image
sudo docker run --name faceplugin -d -p 0.0.0.0:8888:8888 face-livenessGet machine code
Contact us to get the license according to the machine code.
Activate the sever by using the license obtained from us.
Liveness detection using raw file
APIs
get_machine_code: This API is used to retrieve the code specific to the server on which this SDK is running
@app.route('/get-machine-code', methods=['GET'])
def get_machine_code():
machine_code = getMachineCode()
response = jsonify({"machineCode": machine_code.decode("utf-8")})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return responseInput
None
Return value
The string specific to the server on which this SDK is running
activate_machine: This API is used to activate the SDK
@app.route('/activate-machine', methods=['POST'])
def activate_machine():
content = request.get_json()
license = content['license']
ret = setActivation(license.encode('utf-8'))
activate_state = ret
print("activation: ", ret)
ret = initSDK("data".encode('utf-8'))
init_state = ret
print("init: ", ret)
response = jsonify({"activationStatus": activate_state}, {'initStatus': init_state})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return responsecheck_liveness: This API is used to determine if the faces are real or fake. This also returns if the faces are occuluded, if the mouth is open, if the eyes are closed, if the face image quality is good and if the face is fronted or not.
@app.route('/liveness-detection', methods=['POST'])
def check_liveness():
faces = []
isNotFront = None
isOcclusion = None
isEyeClosure = None
isMouthOpening = None
isBoundary = None
isSmall = None
quality = None
luminance = None
livenessScore = None
file = request.files['file']
try:
image = Image.open(file)
except:
result = "Failed to open file"
faceState = {"is_not_front": isNotFront, "is_occluded": isOcclusion, "eye_closed": isEyeClosure, "mouth_opened": isMouthOpening,
"is_boundary_face": isBoundary, "is_small": isSmall, "quality": quality, "luminance": luminance, "result": result, "liveness_score": livenessScore}
response = jsonify({"face_state": faceState, "faces": faces})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return response
image_np = np.asarray(image)
faceBoxes = (FaceBox * maxFaceCount)()
faceCount = faceDetection(image_np, image_np.shape[1], image_np.shape[0], faceBoxes, maxFaceCount)
for i in range(faceCount):
landmark_68 = []
for j in range(68):
landmark_68.append({"x": faceBoxes[i].landmark_68[j * 2], "y": faceBoxes[i].landmark_68[j * 2 + 1]})
faces.append({"x1": faceBoxes[i].x1, "y1": faceBoxes[i].y1, "x2": faceBoxes[i].x2, "y2": faceBoxes[i].y2,
"liveness": faceBoxes[i].liveness,
"yaw": faceBoxes[i].yaw, "roll": faceBoxes[i].roll, "pitch": faceBoxes[i].pitch,
"face_quality": faceBoxes[i].face_quality, "face_luminance": faceBoxes[i].face_luminance, "eye_dist": faceBoxes[i].eye_dist,
"left_eye_closed": faceBoxes[i].left_eye_closed, "right_eye_closed": faceBoxes[i].right_eye_closed,
"face_occlusion": faceBoxes[i].face_occlusion, "mouth_opened": faceBoxes[i].mouth_opened,
"landmark_68": landmark_68})
result = ""
if faceCount == 0:
result = "No face"
elif faceCount > 1:
result = "Multiple face"
else:
livenessScore = faceBoxes[0].liveness
if livenessScore > livenessThreshold:
result = "Real"
else:
result = "Spoof"
isNotFront = True
isOcclusion = False
isEyeClosure = False
isMouthOpening = False
isBoundary = False
isSmall = False
quality = "Low"
luminance = "Dark"
if abs(faceBoxes[0].yaw) < yawThreshold and abs(faceBoxes[0].roll) < rollThreshold and abs(faceBoxes[0].pitch) < pitchThreshold:
isNotFront = False
if faceBoxes[0].face_occlusion > occlusionThreshold:
isOcclusion = True
if faceBoxes[0].left_eye_closed > eyeClosureThreshold or faceBoxes[0].right_eye_closed > eyeClosureThreshold:
isEyeClosure = True
if faceBoxes[0].mouth_opened > mouthOpeningThreshold:
isMouthOpening = True
if (faceBoxes[0].x1 < image_np.shape[1] * borderRate or
faceBoxes[0].y1 < image_np.shape[0] * borderRate or
faceBoxes[0].x1 > image_np.shape[1] - image_np.shape[1] * borderRate or
faceBoxes[0].x1 > image_np.shape[0] - image_np.shape[0] * borderRate):
isBoundary = True
if faceBoxes[0].eye_dist < smallFaceThreshold:
isSmall = True
if faceBoxes[0].face_quality < lowQualityThreshold:
quality = "Low"
elif faceBoxes[0].face_quality < hightQualityThreshold:
quality = "Medium"
else:
quality = "High"
if faceBoxes[0].face_luminance < luminanceDarkThreshold:
luminance = "Dark"
elif faceBoxes[0].face_luminance < luminanceLightThreshold:
luminance = "Normal"
else:
luminance = "Light"
faceState = {"is_not_front": isNotFront, "is_occluded": isOcclusion, "eye_closed": isEyeClosure, "mouth_opened": isMouthOpening,
"is_boundary_face": isBoundary, "is_small": isSmall, "quality": quality, "luminance": luminance, "result": result, "liveness_score": livenessScore}
response = jsonify({"face_state": faceState, "faces": faces})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return responseLast updated