Transcript Face Recognition Method of OpenCV

Face Recognition
Method of
OpenCV
ANDREW STODGHILL
Overview
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Introduction
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Background
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Theory
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Experiment
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Results
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Conclusion
Introduction
Idea
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Facial recognition for a Security Robot
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Addition to another project
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Gain a better understanding of the subject
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Derived from work last semester
Focus
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Basic understand of OpenCV face recognition software and
algorithms
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Methods and Theory behind the EigenFace method for facial
recognition
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Implementation using Python in a Linux-based environment
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Runs on a Raspberry Pi
Goal
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One half research
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General facial recognition methods
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EigenFaces
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OpenCV’s facial recognition
One half implementation
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Create a system capable of facial recognition
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Real-time
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Able to run on a Raspberry Pi
Background
Different Facial Recognition
Methods
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Geometric
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Eigenfaces
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Fisherfaces
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Local Binary Patterns
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Active Appearance
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3D Shape Models
Geometric
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First method of facial recognition
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Done by hand at first
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Find the locations of key parts of the face
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Automation came later
And the distances between them
Good initial method, but had flaws
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Unable to handle multiple views
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Required good initial guess
Eigenfaces
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Information theory approach
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Codes and then decodes face
images to gain recognition
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Uses principal component
analysis (PCA) to find the most
important bits
Fisherfaces
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Same approach as Eigenface
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Instead of PCA, uses linear
discriminant analysis (LDA)
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Better handles intrapersonal
variability within images such
as lighting
Local Binary Patterns
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Describes local features of an object
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Comparison of each pixel to its neighbors
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Histogram of image contains information about the
destruction of the local micro patterns
Theory
Basic Idea
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Let face image 𝐼(𝑥, 𝑦) be a two-dimensional 𝑁 by 𝑁 array of (8-bit)
intensity values
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Can consider image an 𝑁 2 vector of dimensions
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Image of 256 by 256 becomes a 65,536 vector of dimension
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Or a point in 65,536-dimensional space
Basic Idea
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Images of faces will not differ too much
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This allows a much smaller dimensional subspace to be used to
classify them
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PCA analysis finds the vectors that best define the distribution of
images
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These vectors are then
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𝑁 2 long
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Describe an 𝑁 by 𝑁 image
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Linear combination of the original face images
Basic Idea
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These vectors are called eigenfaces
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They are the eigenvectors of the covariance matrix
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Resemble faces
Method
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Acquire initial training set of face images
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Calculate eigenfaces
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Keep only 𝑀 eigenfaces that correspond to the highest eigenvalues
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These images now define the face space
Calculate corresponding distribution in 𝑀-dimensional weight space
for each known individual
Method
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Calculate weights for new image by projecting the input image
onto each of the eigenfaces
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Determine if face is known
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Within some tolerance, close to face space
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If within face space, classify weights as either known or unknown
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(Optional) Update eigenfaces and weights
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(Optional) If same face repeats, input into known faces
Classifying
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Four possibilities for an input image
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Near face space, near face class
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Near face space, not near face class
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Unknown face
Not near face space, near face class
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Known face
Not a face, but may look like one (false positive)
Not near face space, not near face class
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Not a face
OpenCV and Theory
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Beauty about OpenCV is a lot of this process is completely
automated
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Need:
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Training images
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Specify type of training
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Number of eigenfaces
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Threshold
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Input Image
Experiment
Set-up
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OpenCV running on a Raspberry Pi
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Linux-based environment
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Raspberry Pi Camera
Training
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Database of negatives
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AT&T Laboratories Database of faces developed in the 90s
Training
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Captured Positives
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Used the camera to capture images
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Images were then cropped and resized
Training
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Model was trained using positive and negative images
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Creates training file that holds the 𝑀-dimensional face space
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Now have a base to recognize from
model = cv2.createEigenFaceRecognizer()
model.train(np.asarray(faces),np.asarray(labels))
Recognition
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Steps to recognizing face
model = cv2.createEigenFaceRecognizer()
model.load(config.TRAINING_FILE)
label, confidence = model.predict(image)
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Capture image
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Detect face
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Crop and resize around face
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Project across all eigenvectors
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Find face class that minimizes Euclidian distance
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Return label from face class, and Euclidian distance
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Euclidian distance also called Confidence level
Test
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Created four different Test
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First data set uses 24 positive training images
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Second data set uses 12 positive training images
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Good pose variation, little lighting variation
Third data set uses 25 positive training images
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Almost no pose and lighting variation
Good pose and lighting variation
Fourth data set uses second and third data set but with Fisherface
method
Results
Results
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Results from Data sets 1-3, each one from 20 input images
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Confidence represents distance from known face class
Data Set
Mean Confidence Max Confidence
Min Confidence
1
3462
3948
3040
2
2127
2568
1835
3
1709
2196
1217
Results
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Results from eigenface vs. fisherface comparison
Algorithm
Data Set
# Training
Images
Mean
Confidence
Max
Confidence
Min
Confidence
Eigen
2
12
2127
2568
1835
Fisher
2
12
2029
2538
1468
Eigen
3
25
1709
2196
1217
Fisher
3
25
2017
2748
1530
Conclusion
Conclusion
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Theory behind eigenfaces
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Face space
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Training
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Simple implementation of OpenCV’s eigenface recognizer
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Compared different training models
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Number of training images
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Pose and lighting variations
Compared eigenfaces and fisherfaces
Conclusion
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Future work
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Further testing of different training models
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Implement updating facial recognition
Questions?