Computer Vision - Face Recognition

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Computer VisionFace Recognition

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In today's digital age, face recognition technologyhas gained significant attention due to its potential applications in various fields.In this presentation, we will talk about an overview of face recognition, including its definition, principles, and real-world applications. Let's dive in and explore the fascinating world of face recognition!

Face Recognition

Real-Life Applications

EigenFace Pros and Cons

EigenFace VS CNNs

EigenFace Technology

Principles

History

Topics

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Eigenfaces and PCA:

• In 1991, Turk and Pentland introduced the concept of eigenfaces, a popular method for feature extraction in face recognition
• Eigenfaces use principal component analysis (PCA) to represent faces as linear combinations of basis vectors

Early approach :

• Early efforts in face recognition focused on manual measurement and analysis of facial features.
• In the 1960s and 1970s, computer-based techniques were developed, such as photogrammetry and edge detection

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The implementation of facial recognition has seen many iterations, which saw roots in the 1960s when facial recognition was manually implemented by Woodrow Wilson Bledsoe

Deep Learning and Current Advances:

• In recent years, deep learning techniques, particularly convolutional neural networks (CNNs), have shown promising results in face recognition

• Deep learning-based methods have achieved state-of-the-art performance on benchmark datasets, and are being used in a variety of applications such as smartphone authentication and border control

Viola-Jones and Face Detection:

• In 2001, Viola and Jones introduced the Viola-Jones algorithm, a popular method for face detection based on Haar-like features and a classifier

• Viola-Jones was faster and more accurate than previous methods, and has been widely used in applications such as digital cameras and surveillance systems

Face Recognition Principles

The principles of face recognition involve a number of key steps, including:

• Feature extraction:

Feature extraction involves identifying key features of a face that can be used for identification, such as the eyes, nose, and mouth.

• Classification:

Once features have been extracted, a classification algorithm is used to match the extracted features to a known set of faces.

• Face detection:

Before face recognition can be performed, the location and size of faces in an image or video must be determined, and this is the work of Face detection.

• Database management:

In order to perform face recognition, a database of known faces must be maintained. This can be challenging in practice, particularly as the size of the database grows.

• performance evaluation :

The accuracy and reliability of face recognition algorithms must be evaluated using appropriate metrics and datasets.

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Eigenfaces is a technique used in computer vision and facial recognition to extract key features from images of faces .

• The technique involves finding the principal components of a set of training images of faces to create a compact representation of each face.

• Eigenfaces are essentially a set of eigenvectors that are derived from the covariance matrix of a set of face images.

• Each eigenface corresponds to a different eigenvalue, which represents the amount of variation in the dataset that is explained by that particular eigenvector.

• The eigenvectors are arranged in order of decreasing eigenvalues, and the first few eigenvectors, which account for most of the variation in the data, are referred to as the principal components.

Eigenface Technology

We talked about eigenface, but it is an old fasion technology,not accurate when it comes to handling lighting changes, pose variations, and occlusion.In the other hand CNNs are a more modern and powerful method for face recognition, based on deep learning.

• They work by training a neural network to extract high-level features from face images, which are used to classify or identify the image.

• CNNs can be more accurate and robust than traditional methods, especially when dealing with complex tasks and variations in lighting, pose, and expression.

Overall, while eigenfaces and CNNs are both used for face recognition, CNNs are generally considered to be a more powerful and advanced method, especially for complex tasks and real-world applications. However, eigenfaces may still be a useful and efficient method for certain applications, especially in cases where computational resources are limited.

Eigenface and CNN?

EigenFace Pro's and Con's

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Pors:

• Eigenfaces is a relatively simple and computationally efficient method for face recognition, based on linear algebra.

• It can be effective at handling variations in lighting, pose, and expression in face images, especially for simpler tasks and datasets.

• It is a well-established and widely used method, with many resources and libraries available for implementation.

• Eigenfaces can be a useful and practical solution for applications with limited computational resources, such as mobile or embedded devices.

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Every technology has it's Advatages and disadvantages now we will review the Disadvantages of EiegenFace technology:Cons:

• Eigenfaces may not perform as well as more advanced methods, such as convolutional neural networks (CNNs), on complex tasks and datasets.

• It may be less effective at handling variations in face features that are not captured by linear combinations of pixel values, such as skin texture or eye shape.

• Eigenfaces can be sensitive to variations in image quality, such as noise or blur, which can degrade performance.

• It requires a large amount of training data to achieve good performance, which can be a challenge in some applications.

Real World Application

• Technology that verifies a person's identity through facial recognition.

• Advantages: high accuracy, convenience, scalability, security.

• Challenges: privacy concerns, potential bias, reliability issues, cost.

• Can be a useful and effective solution for controlling access to physical spaces, devices, or applications.

• Implementation must be responsible and ethical.

Access Control Using Face Recognition

• Face recognition used for patient identification and record management in hospitals.

• Can be used to monitor patient adherence to medication and treatment plans.

• Improves accuracy and efficiency of medical records and reduces errors.

• Raises concerns around privacy and security of medical data.

HealtCare

• Face recognition used in law enforcement and security systems for identification, tracking, and monitoring individuals of interest.

• Can be used to identify suspects in criminal investigations or locate missing persons.

• Enables faster and more accurate identification than traditional methods.

• Raises concerns around privacy and potential misuse.

Security and surveillance

Anas Alemam

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