Real-time Face Detection and Recognition
•
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Code: https://github.com/amitra93/FaceRecognition
![Zelun Luo, Anarghya Mitra
Mentor: Jia-Bin Huang. Advisor: Narendra Ahuja.
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Make a robust system capable of identifying multiple faces with a
learning algorithm for identifying faces not in its database.
Integral Image
Cascade Architecture
Haar-like Features
• Very few faces in an image
• Most sub-windows rejected
early since they are not
faces
• Each successive classifier
is trained only on those
selected samples which
pass through the
preceding classifiers.
s(1) = A
s(2) = A + B
s(3) = A + C
s(4) = A + B + C + D
The sum within D can be
computed as:
s(4)+s(1)-s(2)-s(3).
(x, y)
A B
C D
1 2
3 4
The value of the integral image
at point (x, y) is the sum of all
the pixels above and to the left
of x, y, inclusive:
where s(x, y) is the integral
image and i(x, y) is the original
image.
• The area around the
eyes is lighter than
the eyes itself – i.e.
the nose is brighter
than the eyes on a
normalized graph.
• The area on top and
below the eyes is
lighter than the
eyes.
Our face can be used to control
computers. One example is a
game we can play with our
nose acting as the mouse.
Main ideas -
• Very few faces in an image => cascade structure of weak
classifiers
• Evaluate haar-like features in O(1) time using integral
image
• Select discriminative features with Adaboost
[Viola & Jones, 2004] Viola, P. & Jones, M. J. (2004). Robust real-time face
detection. International journal of computer vision, 57(2), 137–154.
[Yang, 2002] Yang, M.-H. (2002). Kernel eigenfaces vs. kernel fisherfaces:
Face recognition using kernel methods. In Proceedings of the Fifth IEEE
International Conference on Automatic Face and Gesture Recognition
Original images
• Eigenfaces encode the variation
in the training set.
• Each image can be represented
using a linear combination of
eigenfaces.
Can be used in identity
verification along with
fingerprint and iris recognition
systems.
Reconstruction
using eigenfaces](https://image.slidesharecdn.com/purenew-130527120104-phpapp02/85/realtime-face-detection-and-recognition-1-320.jpg)
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Real-time Face Detection and Recognition
- 1. Zelun Luo, Anarghya Mitra Mentor: Jia-Bin Huang. Advisor: Narendra Ahuja. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Make a robust system capable of identifying multiple faces with a learning algorithm for identifying faces not in its database. Integral Image Cascade Architecture Haar-like Features • Very few faces in an image • Most sub-windows rejected early since they are not faces • Each successive classifier is trained only on those selected samples which pass through the preceding classifiers. s(1) = A s(2) = A + B s(3) = A + C s(4) = A + B + C + D The sum within D can be computed as: s(4)+s(1)-s(2)-s(3). (x, y) A B C D 1 2 3 4 The value of the integral image at point (x, y) is the sum of all the pixels above and to the left of x, y, inclusive: where s(x, y) is the integral image and i(x, y) is the original image. • The area around the eyes is lighter than the eyes itself – i.e. the nose is brighter than the eyes on a normalized graph. • The area on top and below the eyes is lighter than the eyes. Our face can be used to control computers. One example is a game we can play with our nose acting as the mouse. Main ideas - • Very few faces in an image => cascade structure of weak classifiers • Evaluate haar-like features in O(1) time using integral image • Select discriminative features with Adaboost [Viola & Jones, 2004] Viola, P. & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137–154. [Yang, 2002] Yang, M.-H. (2002). Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods. In Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition Original images • Eigenfaces encode the variation in the training set. • Each image can be represented using a linear combination of eigenfaces. Can be used in identity verification along with fingerprint and iris recognition systems. Reconstruction using eigenfaces