Face recognition is a technology that involves identifying or verifying the identity of a person by analyzing and comparing patterns in their facial features. This process typically involves the use of computer algorithms and machine learning techniques, such as neural networks, to analyze facial images and extract key features that are unique to each individual's face. These features are then compared against a database of known faces to determine the identity of the person in question.

1. FA C E
S C O P E
Brainwaves team

2. ‫هللا‬ ‫بسم‬ ‫يال‬

3. I N T R O D U C T I O N
Face recognition technology is a biometric
identification method that uses an individual's facial
features to identify and verify their identity. The technology
has become increasingly popular in recent years as a more
secure and convenient alternative to traditional
identification methods like passwords and PINs.
One application of face recognition technology is in the
context of attendance tracking. With the use of face
recognition, organizations can automate attendance
tracking by allowing employees or students to simply scan
their faces to mark their attendance instead of manually
signing in or using a card-based system.
Using face recognition for attendance tracking has many
benefits. It eliminates the need for physical touch and
reduces the risk of fraud, as it is difficult to forge
someone's face. It also saves time and reduces errors, as
the attendance data is automatically collected and
processed.
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4. M A C H I N E L E A R N I N G
Machine learning is a subset of artificial intelligence that involves
training algorithms to learn patterns and make predictions or
decisions based on data. It is a way of teaching computers to
recognize patterns in data and use that knowledge to make
decisions or predictions about new data.
At its core, machine learning involves using mathematical
models and algorithms to analyze data, identify patterns, and
make predictions or decisions. The algorithms are designed to
iteratively learn from the data they are fed, adjusting their models
to improve accuracy and reduce errors over time.
Machine learning is used in a wide range of applications, from
image recognition and natural language processing to fraud
detection and predictive analytics. Some common examples
include recommendation systems, chatbots, and autonomous
vehicles.
There are several different types of machine learning, including
supervised learning, unsupervised learning, and reinforcement
learning. Each type of machine learning involves different
methods for training algorithms to learn from data and make
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5. Machine learning
Deep learning

6. D E E P L E A R N I N G
Deep learning is a subfield of machine learning that is inspired
by the structure and function of the human brain. It uses artificial
neural networks to learn and model complex patterns in data.
In deep learning, neural networks are composed of multiple
layers of interconnected nodes, which are called neurons. Each
neuron receives input from multiple other neurons in the previous
layer and produces an output that is passed on to the next layer.
By adjusting the weights of the connections between neurons, the
network can learn to recognize patterns and make predictions.
Deep learning has been applied successfully in a variety of
domains, including computer vision, natural language processing,
and speech recognition. For example, deep learning models can
be trained to recognize objects in images, translate text from one
language to another, or transcribe speech into text.
One of the key advantages of deep learning is its ability to
automatically extract useful features from raw data, without the
need for manual feature engineering. This makes it particularly
useful for handling large and complex datasets, where it can
discover hidden patterns and relationships that might be difficult
for humans to identify.
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7. S T E P S O F F A C E
R E C .
1) Face Detection: The first step in face recognition is to locate and
extract the face from an image or video frame. This is done using face
detection algorithms that identify the presence and location of faces in
an image.
2) Face Alignment: Once the face is detected, it is important to align it in
a standard position and scale to ensure consistent and accurate
feature extraction. This is done using techniques such as
normalization, pose estimation, and landmark detection.
3) Feature Extraction: The next step is to extract meaningful features
from the aligned face image. This is typically done by using deep
learning models, which can learn to identify and extract relevant
features from the face, such as the shape of the eyes, nose, and
mouth.
4) Feature Matching: Once the features are extracted, the next step is to
compare them with the features of known faces in the database to find
a match. This is done using algorithms such as the Euclidean distance,
cosine similarity, or support vector machines.
5) Recognition: Finally, based on the feature matching results, the face
is identified and labeled with the corresponding name or identity. In
some cases, the system may also perform additional verification steps,
such as asking the user to perform a secondary authentication factor,
to ensure the accuracy and security of the recognition result
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8. ‫ليفة‬ ‫يا‬ ‫خش‬

9. U I ( U S E R I N T E R F A C E )
Tkinter is a Python library used for creating graphical user
interfaces (GUI). It provides a set of tools and widgets for building
desktop applications that can run on multiple platforms, including
Windows, macOS, and Linux.
With Tkinter, you can create windows, buttons, labels, textboxes,
and other GUI elements that allow users to interact with your
application. Tkinter also supports event-driven programming,
which means that your application can respond to user actions,
such as button clicks or key presses.
Some of the key features of Tkinter include:
Cross-platform compatibility: Tkinter is included with Python,
which means that your application can run on any platform that
supports Python.
Easy to use: Tkinter has a simple and intuitive interface, making
it easy to learn and use even for beginners.
Customizable widgets: Tkinter provides a variety of widgets that
can be customized to fit your application's needs. You can also
create your own custom widgets if needed.
Support for event-driven programming: Tkinter supports the use
of events and callbacks, which makes it easy to respond to user
actions and update the GUI in real-time.
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10. U I ( C O N T. )
PIL (Python Imaging Library) is a Python library used for working with
images. It provides a wide range of image processing functionalities, such
as opening and saving images in various formats, manipulating images,
applying filters and transformations, and generating image thumbnails.
PIL is a powerful tool for working with images in Python, and it is often
used in computer vision and machine learning applications that involve
image processing. Some of the key features of PIL include:
1. Image I/O: PIL provides support for opening and saving images in a
variety of formats, including JPEG, PNG, BMP, TIFF, and GIF.
2. Image manipulation: PIL provides a set of tools for manipulating
images, such as resizing, cropping, rotating, and flipping.
3. Image filtering: PIL includes a range of filters that can be applied to
images, such as blurring, sharpening, and edge detection.
4. Image transformations: PIL provides support for transforming images,
such as changing the color space, adjusting brightness and contrast, and
applying geometric transformations.
5. Image enhancement: PIL includes tools for enhancing images, such as
histogram equalization and color balance adjustment.
Overall, PIL is a powerful library for working with images in Python.
Whether you're building a computer vision application, a machine learning
model, or simply working with images in your Python project, PIL can
provide the tools you need to manipulate and process images efficiently.
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11. back
UI
back

12. B A C K
`Encodings : encodings refer to the process of
representing data in a specific format, such as binary or
ASCII. An encoding is a mapping between a set of
characters or symbols and their binary representations.
converts each image from BGR format (which OpenCV
uses by default) to RGB format
obtain the facial features for each face present in the
image
marks the attendance  CSV file
reads the existing lines in the CSV file
strftime()
bounding box around detected faces
bounding box around detected faces
extracts the region of interest (ROI) for each face
level greater than 60% , 50-60%, a red bounding box
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13. B A C K
`1. Imports the reperforms real-time face recognition
using the LBPH
quired libraries (`cv2` for computer vision and `time` for measuring FPS).
video capture was successful. The function detects faces in the image,
draws a rectangle around them, and returns the image.
3. Loads the pre-trained classifier from a file.
Calls the `findEncodings` method to encode the images of known faces.
Starts capturing video from the default camera (`cv2.VideoCapture(0)`).
. In an infinite loop:
1. Reads a frame from the video capture.
2. Calls the `recognize` function to detect faces and draw rectangles around
them.
3. Calculates the FPS (frames per second) and displays it on the screen.
4. Displays the processed image on the screen.
5. Waits for a key press to quit the loop.
7. Releases the video capture and destroys all windows. Sample Footer Text 13