Face Recognition Method of OpenCV Andrew stodghill

Overview Introduction Background Theory Experiment Results Conclusion

Introduction

Idea Facial recognition for a Security Robot Addition to another project Gain a better understanding of the subject Derived from work last semester

Focus Basic understand of OpenCV face recognition software and algorithms Methods and Theory behind the EigenFace method for facial recognition Implementation using Python in a Linux-based environment Runs on a Raspberry Pi

Goal One half research One half implementation General facial recognition methods EigenFaces OpenCV’s facial recognition One half implementation Create a system capable of facial recognition Real-time Able to run on a Raspberry Pi

Background

Different Facial Recognition Methods Geometric Eigenfaces Fisherfaces Local Binary Patterns Active Appearance 3D Shape Models

Geometric First method of facial recognition Done by hand at first Automation came later Find the locations of key parts of the face And the distances between them Good initial method, but had flaws Unable to handle multiple views Required good initial guess

Eigenfaces Information theory approach Codes and then decodes face images to gain recognition Uses principal component analysis (PCA) to find the most important bits

Fisherfaces Same approach as Eigenface Instead of PCA, uses linear discriminant analysis (LDA) Better handles intrapersonal variability within images such as lighting

Local Binary Patterns Describes local features of an object Comparison of each pixel to its neighbors Histogram of image contains information about the destruction of the local micro patterns

Theory

Basic Idea Let face image 𝐼(𝑥,𝑦) be a two-dimensional 𝑁 by 𝑁 array of (8-bit) intensity values Can consider image an 𝑁 2 vector of dimensions Image of 256 by 256 becomes a 65,536 vector of dimension Or a point in 65,536-dimensional space

Basic Idea Images of faces will not differ too much This allows a much smaller dimensional subspace to be used to classify them PCA analysis finds the vectors that best define the distribution of images These vectors are then 𝑁 2 long Describe an 𝑁 by 𝑁 image Linear combination of the original face images

Basic Idea These vectors are called eigenfaces They are the eigenvectors of the covariance matrix Resemble faces

Method Acquire initial training set of face images Calculate eigenfaces Keep only 𝑀 eigenfaces that correspond to the highest eigenvalues These images now define the face space Calculate corresponding distribution in 𝑀-dimensional weight space for each known individual

Method Calculate weights for new image by projecting the input image onto each of the eigenfaces Determine if face is known Within some tolerance, close to face space If within face space, classify weights as either known or unknown (Optional) Update eigenfaces and weights (Optional) If same face repeats, input into known faces

Classifying Four possibilities for an input image Near face space, near face class Known face Near face space, not near face class Unknown face Not near face space, near face class Not a face, but may look like one (false positive) Not near face space, not near face class Not a face

OpenCV and Theory Beauty about OpenCV is a lot of this process is completely automated Need: Training images Specify type of training Number of eigenfaces Threshold Input Image

Experiment

Set-up OpenCV running on a Raspberry Pi Linux-based environment Raspberry Pi Camera

Training Database of negatives AT&T Laboratories Database of faces developed in the 90s

Training Captured Positives Used the camera to capture images Images were then cropped and resized

Training Model was trained using positive and negative images Creates training file that holds the 𝑀-dimensional face space Now have a base to recognize from model = cv2.createEigenFaceRecognizer() model.train(np.asarray(faces),np.asarray(labels))

Recognition Steps to recognizing face Capture image Detect face Crop and resize around face Project across all eigenvectors Find face class that minimizes Euclidian distance Return label from face class, and Euclidian distance Euclidian distance also called Confidence level model = cv2.createEigenFaceRecognizer() model.load(config.TRAINING_FILE) label, confidence = model.predict(image)

Test Created four different Test First data set uses 24 positive training images Almost no pose and lighting variation Second data set uses 12 positive training images Good pose variation, little lighting variation Third data set uses 25 positive training images Good pose and lighting variation Fourth data set uses second and third data set but with Fisherface method

Results

Results Results from Data sets 1-3, each one from 20 input images 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 Results from eigenface vs. fisherface comparison Algorithm Data Set # Training Images Mean Confidence Max Confidence Min Confidence Eigen 2 12 2127 2568 1835 Fisher 2029 2538 1468 3 25 1709 2196 1217 2017 2748 1530

Conclusion

Conclusion Theory behind eigenfaces Face space Training Simple implementation of OpenCV’s eigenface recognizer Compared different training models Number of training images Pose and lighting variations Compared eigenfaces and fisherfaces

Conclusion Future work Further testing of different training models Implement updating facial recognition

Questions?