Eigenfaces In Scala
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This document provides an overview of the Eigenfaces algorithm for facial recognition. It discusses that Eigenfaces involves obtaining a training set of faces, computing the mean image, subtracting that mean from each image to get a differential matrix, computing the covariance matrix from that, and then calculating eigenvectors and Eigenfaces from the covariance matrix. It also notes that preprocessing images through grayscale, normalization, and cropping is important. Finally, it mentions that the author once used a modified Eigenfaces approach to automate facial matching on the Tinder dating app.
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Real time face recognition system using eigen faces
This document summarizes a research paper on real-time face recognition using eigenfaces. It discusses:
1. Eigenfaces are a set of eigenvectors used in face recognition to capture variations between face images and efficiently represent faces in a lower-dimensional "face space".
2. The process involves calculating eigenfaces from a training dataset of faces, projecting new images into face space, and comparing them to trained images to identify matches.
3. The paper focuses on implementing face recognition using principal component analysis in Matlab and C#.Net. Eigenfaces are extracted from a training set containing faces with variations in pose, lighting, scale, and features. Faces in new images are detected and matched against the training
Ijcatr04041016
This document presents a method for identity recognition using edge suppression. It aims to recognize identity even under severe shadows. The method uses edge suppression through affine transformation and gradient field calculation to determine light source positions. K-nearest neighbor classification is used for matching identities in databases, along with principal component analysis for feature extraction. Diagonally projecting tensors are applied to suppress edges and remove shadows from images. The method is evaluated on standard databases and is proposed to work for real-time identity recognition applications.
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An image is a medium for conveying information. The information contained therein may be a particular event, experience or moment. Not infrequently many images that have similarities. However, this level of similarity is not easily detected by the human eye. Eigenface is one technique to calculate the resemblance of an object. This technique calculates based on the intensity of the colors that exist in the two images compared. The stages used are normalization, eigenface, training, and testing. Eigenface is used to calculate pixel proximity between images. This calculation yields the feature value used for comparison. The smallest value of the feature value is an image very close to the original image. Application of this method is very helpful for analysts to predict the likeness of digital images. Also, it can be used in the field of steganography, digital forensic, face recognition and so forth.
Volume 2-issue-6-2108-2113
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
Volume 2-issue-6-2108-2113
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
ppt 20BET1024.pptx
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
cvpresentation-190812154654 (1).pptx
This document describes a project to implement real-time facial recognition using OpenCV and Python. The project uses a laptop's webcam to capture video frames and detect and recognize faces in each frame. It trains an image dataset with face images and IDs then detects faces in each new video frame. It predicts faces by comparing features to the training data and labels matches based on a confidence level threshold. The document outlines the use of Haar cascade classifiers, LBPH algorithms, and OpenCV functions to complete the facial recognition process in real-time on new video frames from the webcam.
Image attendance system
This document describes a camera-based classroom attendance system project. The objectives are to take attendance of a classroom using webcams and develop a hardware/software interface. The design methodology involves taking pictures of the classroom with webcams and analyzing them using OpenCV image processing software. The method divides the classroom into parts with one webcam for each, takes photos at intervals, converts them to grayscale, generates student masks, and subtracts photos to identify students based on face outlines. Requirements include suitable illumination and students sitting in the same places. Samples of implementation results are also shown.
IRJET - A Review on Face Recognition using Deep Learning Algorithm
This document provides an overview of face recognition using deep learning algorithms. It discusses how deep learning approaches like convolutional neural networks (CNNs) have achieved high accuracy in face recognition tasks compared to earlier methods. CNNs can learn discriminative face features from large datasets during training to generalize to new images, handling variations in pose, illumination and expression. The document reviews popular CNN architectures and training approaches for face recognition. It also discusses other traditional face recognition methods like PCA and LDA, and compares their performance to deep learning methods.
Rapid object detection using boosted cascade of simple features
1. The document presents the seminal work of Viola and Jones on rapid object detection using boosted cascades of simple features.
2. It introduces integral images for fast feature evaluation and uses AdaBoost for feature selection and classifier training in a cascade structure.
3. The cascade approach combines classifiers such that earlier ones rapidly reject negatives while later ones focus on positives, achieving real-time detection rates.
Lecture01 intro ece
This document provides an introduction to digital image processing. It defines what an image is, including bitmap and vector representations. Bitmap representations store individual pixel values in a grid, while vector representations store object information like circles and lines. The document compares and contrasts these representations. It then discusses the history and applications of image processing, as well as common techniques like enhancement, restoration, segmentation, and description. Finally, it introduces the image processing library and test code used in the course.
Computer Vision - Real Time Face Recognition using Open CV and Python
This project is used for creation,training and detection/recognition of Face Datasets using Machine learning algorithm on Laptop’s Webcam.
Introduction to Machine Vision
This document provides an introduction and syllabus for a machine vision course. The 2-day course covers MATLAB basics, image processing fundamentals, and 4 projects involving content-based image retrieval, depth from stereo images, image segmentation, and object recognition. The instructor provides several demonstrations of computer vision applications and encourages students to directly start working on projects.
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Eigenfaces , Fisherfaces and Dimensionality_Reduction
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Eigenfaces In Scala
- 1. A quick walkthrough and sample implementation of the Eigenfaces algorithm in Scala. Justin Long
- 2. What is Eigenfaces? Need to do quick and dirty object or facial recognition? Eigenfaces may be for you.
- 3. The essence of Eigenfaces Eigenfaces is the name given to a set of eigenvectors when they are used for facial recognition. A typical use for calculating Eigenfaces works as such: 1. Obtain a training set of faces and convert to a pixel matrix 2. Compute the mean image (which is an average of pixel intensity across each image). 3. Compute a differential matrix by subtracting the mean from each training image, pixel by pixel 4. Compute covariance matrix of the differential matrix
- 4. The essence of Eigenfaces Your “average face” may look a little uncanny… 4. Calculate eigenvectors from covariance matrix 5. Compute Eigenfaces by multiplying eigenvectors and covariance matrices together, and normalizing them
- 5. Putting it together def computeEigenFaces(pixelMatrix: Array[Array[Double]], meanColumn: Array[Double]): DoubleMatrix2D = { val diffMatrix = MatrixHelpers.computeDifferenceMatrixPixels(pixelMatrix, meanColumn) val covarianceMatrix = MatrixHelpers.computeCovarianceMatrix(pixelMatrix, diffMatrix) val eigenVectors = MatrixHelpers.computeEigenVectors(covarianceMatrix) computeEigenFaces(eigenVectors, diffMatrix) }
- 6. Multiplying eigenvectors/differential (0 to (rank-1)).foreach { i => var sumSquare = 0.0 (0 to (pixelCount-1)).foreach { j => (0 to (imageCount-1)).foreach { k => eigenFaces(j)(i) += diffMatrix(j)(k) * eigenVectors.get(i,k) } sumSquare += eigenFaces(j)(i) * eigenFaces(j)(i) } var norm = Math.sqrt(sumSquare) (0 to (pixelCount-1)).foreach { j => eigenFaces(j)(i) /= norm } }
- 7. Preprocessing is key You need to preprocess your images! - Grayscale: important for calculating proper pixel intensity values - Normalization: very helpful if your images were taken in different lighting conditions - Cropping: Very important to focus only on facial features Without preprocessing, you’re gonna have a bad time.
- 8. Potential for Eigenfaces? Facial recognition is most obvious use. Eigenfaces and eigenvectors are also useful for other applications of computer vision: - Subjects that can be read 2-dimensionally, from same angle - Optical Character Recognition (OCR) - Image segmentation - http://www.cs.huji.ac.il/~yweiss/iccv99.pdf
- 9. What about this author? Well… I had a more creative use for Eigenfaces…
- 10. Tinderbox used Eigenfaces What happened when I got fed up with all the swiping involved in Tinder? I automated it with Eigenfaces (a slightly modified strategy). - Added a simple step of averaging all faces together for yes/no faces - The “average” face was used for Eigenfaces selection (k-nearest neighbor between 2 models)
- 11. A quick look at Tinderbox http://crockpotveggies.com/2015/02/09/automating-tinder-with- eigenfaces.html
- 12. Questions?