Bi model face recognition framework
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This document proposes a bi-modal face recognition framework that uses principal component analysis (PCA) and fisher linear discriminant analysis (FLDA) to build an expression invariant model. It first discusses treating expressions as either noise or extra features. It then outlines using PCA to reduce dimensionality and extract low-dimensional facial features, and FLDA to maximize discrimination between classes. The document provides walkthroughs of how PCA works by identifying patterns in data and reducing dimensions without much information loss. It explains that FLDA finds projections that maximize distance between class means while accounting for within-class scatter. Fusion of modalities is mentioned but not described in detail.
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Resnet.pdf
ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep.
The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem.
ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).
Illustrative Introductory Neural Networks
An introductory/illustrative but precise slide on mathematics on neural networks (densely connected layers).
Please download it and see its animations with PowerPoint.
*This slide is not finished yet. If you like it, please give me some feedback to motivate me.
I made this slide as an intern in DATANOMIQ Gmbh
URL: https://www.datanomiq.de/
Resnet.pptx
ResNet (short for Residual Network) is a deep neural network architecture that has achieved significant advancements in image recognition tasks. It was introduced by Kaiming He et al. in 2015.
The key innovation of ResNet is the use of residual connections, or skip connections, that enable the network to learn residual mappings instead of directly learning the desired underlying mappings. This addresses the problem of vanishing gradients that commonly occurs in very deep neural networks.
In a ResNet, the input data flows through a series of residual blocks. Each residual block consists of several convolutional layers followed by batch normalization and rectified linear unit (ReLU) activations. The original input to a residual block is passed through the block and added to the output of the block, creating a shortcut connection. This addition operation allows the network to learn residual mappings by computing the difference between the input and the output.
By using residual connections, the gradients can propagate more effectively through the network, enabling the training of deeper models. This enables the construction of extremely deep ResNet architectures with hundreds of layers, such as ResNet-101 or ResNet-152, while still maintaining good performance.
ResNet has become a widely adopted architecture in various computer vision tasks, including image classification, object detection, and image segmentation. Its ability to train very deep networks effectively has made it a fundamental building block in the field of deep learning.
Recognition
This document discusses object recognition and summarizes several key concepts:
1. Object recognition includes tasks like verification, detection, identification, and categorization of objects and scenes.
2. Dimensionality reduction techniques like principal component analysis (PCA) can be used to reduce the dimensionality of image data and extract the most important variations, called eigenfaces, for face recognition.
3. A Bayesian framework can be used for probabilistic classification, where the posterior probability of a class is calculated using the likelihood, prior, and evidence to determine the most probable class.
fuzzy LBP for face recognition ppt
This document presents a novel fuzzy feature extraction method using Local Binary Patterns (LBP) for face recognition. It begins with an introduction to face recognition challenges and common approaches. It then describes using LBP to extract local features from divided image windows. Features are extracted by computing membership values based on pixel intensities and taking the product with central pixel value. Local and central pixel information values are combined as the total information for classification using Support Vector Machine (SVM) or K-Nearest Neighbor classifiers. Experimental results on two databases show the proposed approach achieves better recognition rates than other methods. The conclusion is that accounting for both central and neighborhood pixel information is effective for images with expression, illumination and pose variations.
Standard Statistical Feature analysis of Image Features for Facial Images usi...
This document compares Principal Component Analysis (PCA) and Independent Component Analysis (ICA) and their application to facial image analysis. It provides an introduction to both PCA and ICA, including their processes and differences. The document then summarizes previous literature comparing PCA and ICA, describes implementations of PCA for facial recognition on Japanese, African, and Asian datasets in MATLAB, and calculates statistical metrics for the original and recognized images. It concludes that PCA is effective for pattern recognition and dimensionality reduction in facial analysis applications.
PRML Chapter 4
This chapter discusses classification methods including linear discriminant functions and probabilistic generative and discriminative models. It covers linear decision boundaries, perceptrons, Fisher's linear discriminant, logistic regression, and the use of sigmoid and softmax activation functions. The key points are:
1) Classification involves dividing the input space into decision regions using linear or nonlinear boundaries.
2) Perceptrons and Fisher's linear discriminant find linear decision boundaries by updating weights to minimize misclassification.
3) Generative models like naive Bayes estimate joint probabilities while discriminative models like logistic regression directly model posterior probabilities.
4) Sigmoid and softmax functions are used to transform linear outputs into probabilities for binary and multiclass classification respectively.
WIA 2019 - Steering Model Selection with Visual Diagnostics
This document summarizes techniques for model selection and evaluation using visual diagnostics. It discusses using visualizations to analyze features, select algorithms, tune hyperparameters, and evaluate model performance. Key aspects covered include using visualizations to identify important and correlated features, determine the best number of clusters or regularization parameter value, and evaluate classifier performance metrics and regression error. The goal is to visually diagnose issues and identify the best modeling choices.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
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Bi model face recognition framework
- 1. Bi-model face recognition framework [For expression invariant face recognition]
- 2. Expressions: Two perspectives. 1) Noise?
- 3. Expressions 11) Extra features ? Essentially everyone has got a different smile here.
- 4. Outline Principal Component Analysis Reduce dimensionality. Extract low dimensional features from original facial appearance. Fisher Linear Discriminant Analysis To maximize the discrimination between different classes. Fusion of Modalities. Well we have not done much of work on this front yet.
- 5. Principal Component Analysis [PCA] What is it? A way to identify patterns in the data. Reduce or compress the data without loss of much information. In our case: introduce generalization. What happens when we increase number of neurons?
- 6. PCA : Walkthrough - 1 Get some data Subtract the mean [Data Adjust].
- 7. PCA : Walkthrough - 2 Data plotted on x-y
- 8. PCA : Walkthrough - 3 Calculate the covariance matrix Calculate the eigenvalues and eigenvectors The eigenvector with highest eigenvalue is Principal Component.
- 9. PCA : Walkthrough - 4
- 10. PCA : Walkthrough - 5
- 11. Fisher Linear Discriminant Analysis PCA finds the most accurate data representation for lower dimensional space. But directions of maximum variance may be useless for classification. #Fail
- 12. FLDA – Main idea Find projection to a line, or plane in case of higher dimension such that different classes are well separated.
- 13. FLDA - 1 Find vector such that the distance between the projection of classes is maximum along that vector. But how to define distance? Good along vertical axis, not so good along horizontal axis
- 14. FLDA - 2 The problem is that we did not consider *scatter* of the data. Ok, wait!! What is scatter? Scatter is variance multiplied by no. of elements.
- 15. FLDA - 3 Fisher solution : Normalize by scatter. Thus Fisher linear discriminant is to project on line in the direction of v which maximizes J(v) is the distance between mean values of two classes which are normalized by scatter. After performing few complicated looking simple vector algebraic steps we get
- 16. FLDA - Example PCA FLDA
- 17. Multiple Modalities Background Rest of the story during final presentation and demo.
- 18. Questions?
- 19. Thank You