Cascade Classification of Face Liveliness Detection using HeartBeat Measurement

1. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Paper ID- xxx
Cascade Classification of Face Liveliness
Detection using HeartBeat Measurement
Md. Mahfujur Rahman1, Shamim Al Mamun2, M. Shamim
Kaiser2, Md. Shahidul Islam2, and Md. Arifur Rahman2
1 Daffodil International University and 2 Jahangirnagar University
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Paper ID - 432

2. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Introduction
• Face detection and recognition is a prevalent concept in security and access
control area which is commonly used in surveillance cameras at public places,
attendance etc.
• But this kind of system can be circumvented by holding a photo or running a
video of authorized person to the camera
• Therefore, liveliness concept comes up with a solution to detect the person is
real or spoofed.
• Deep Learning with HeartBeat Measurement can be solution for detecting
Liveliness.
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3. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Research Objectives
• The main objective of this research to propose a cascade
classifier based model for detecting liveliness using deep-
learning and Heart-beat measurement
• Improved CNN based FaceNet model with the
implementation of heartbeat measurement in liveliness
detection.
• Compare the performance of the Deep Learning classifiers
with State-of-art method.
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4. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Motivation
• Due to the rapid digitalization, nowadays, the bio-metric
authentication technique is used for identity
management.
• Face recognition is more convenient to install than other
bio-metric techniques.
• However, despite its advantage as a non-intrusive form of
access, the security system might not be able to
distinguish between a real person and a person’s
photograph.
• Therefore, early face recognition authentication, face
liveliness detection is important to detect whether the
captured face is an alive or fake captured image.
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5. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Dataset Description
● Dataset for liveliness detection needs diverse set of real and fake images of a person. We collected data from
students of IIT, JU and students of CSE, DIU of Bangladesh and also some faculty members of those
universities.
● In our collected data set we have mainly two classes of images: Fake images and Real images with
individual person identification.
● Data set are collected in two ways: captured images and video file.
● Fake images of a person captured from a camera while playing a video or setting up a spoofed image at
screen in-front of their real face.
● Real images of a person captured from a selfie video/images with phone or camera and also collected heart
rate of a person using garmin 235J fitness watch which is considered as ground truth value.
● We collected two types of videos: Fake videos and Real Videos. one video for “real” faces and another for
“fake/spoofed” faces.
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6. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Model Architecture
• The system provides the security of attendance application by authenticating
the user with face attribute along with aliveness detection and recognition using
MTCNN, CNN based face liveliness detection with Heartbeat Measurement.
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Figure: Face liveliness detection block model

7. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Model Architecture
(Continue)
● In face recognition model, there are three basic ways to recognize a
face such as - Face Detection, Feature Extraction and Face
Classification
● After detecting the face and recognition, we applied heartbeat
measurement (HBM) to verify the face liveliness.
Fig: Major blood supply arteries to the face and our Region of Interest (ROI)
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8. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Liveliness Detection
Face liveliness detection output. The system has taken input as live/spoofed and detected intermediate
output from facenet and finally measured heart-beat to enhance the accuracy of detecting face whether
it is really a live face or not.
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9. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Result and Discussion
• we took 135 live faces and 49 spoofed faces for testing
purposes.
• The corrected Live instances was 127 which was True
Positive and incorrect instances was 8.
• On the other hand, The correctly identified spoofed
faces 42 and incorrectly classified was 7
• Our main objective was to improvement the FaceNet
based CNN model. So output result of Facenet based
CNN model is taken as input in Heartbeat
Measurement Model.
• Heartbeat measurement model correctly identified all
135 live faces which was actually 135 and correctly
identified 48 spoofed faces which was actually 49.
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Figure: Confusion Matrix without heart-beat
measurement and using heart-beat measurement

10. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Result and Discussion
(Continue)
• In this study, false positive rate of FaceNet based CNN model is 16% which is reduced to 0% in
Heartbeat Measurement to detect liveliness.
• False negative rate of FaceNet based CNN model is 5.22% where Heartbeat measurement model
reduced false-negative rate of CNN based FaceNet Model which is 0.74%.
Table 1. Performance comparison of our liveliness detection method with some state-of-arts
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11. 2nd International Conference on Trends in Computational and Cognitive Engineering
(TCCE)
Conclusion
• This paper proposed an identity authentication for attendance system combining a CNN based
FaceNet model and Heart Beat Measurement to improve face liveliness detection against
spoofing attack in authentication system.
• Moreover, this work have achieved 99.46% of accuracy for detecting a spoofed face.
• In future, we will use Bayesian Convolutional Neural Network (BayesCNN) approach to examine
liveliness.
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