Extracting individual information using facial recognition in a smart mirror.pptx

Extracting individual information using facial recognition in a smart mirror
Guide Name: Submitted by
Dr. Sanjay Kumar Iqra Rani

Introduction
• A mirror is a common item that almost everyone owns today. A mirror is
something that the majority of people use every day, whether it is placed in
the living room, bedroom, or bathroom. Thus, it would appear simple to
convert a regular mirror into a smart mirror that can recognise emotional
problems in the user and offer both immediate and long-term support.
• A smart mirror is a two-way mirror with an electronic display behind the
glass. The display can show the viewer different kinds of information in the
form of widgets, such as weather, time, date, and news updates.
• The raspberry pi is programmed using python and connects to a monitor with
inbuilt speaker to provide onscreen interface and voice assistance.
• The smart device can simply perceive, process, and evaluate the information
that has been gathered.

Applications
•Access Control.
•Security and Surveillance.
•Health and Safety.
•Time and Attendance.
•eKYC and Fintech.
•Smart Retail and Personalized
Customer Experiences.
•Law Enforcement.

Proposed Work
• Pre-processing
• Feature Extraction
• Classification of Data

. Pre-processing phase:-
• It is a phase where the image's noise is eliminated. The noise in the image is removed through a Gaussian
filter. In these filters the weights are picked up as per the outline of the Gaussian function.
• The Gaussian functions contain 5 attributes using which they become suitable to process the vision in real-
time. These attributes represent the efficiency of these filters as low-pass filters according to the perception
of spatial and frequency domains.
2. Feature Extraction: -
• In order to extract features, the LBP algorithm is used. The initial computing process in the LBP is of
generating an intermediary image that, by emphasizing the face attributes, more accurately describes the
actual photo. A sliding window is deployed on the basis of radius and neighbors.

 Consider a grayscale image of a face.
 A window of 3x3 pixels can be used to extract a portion of this image.
 The intensity of each pixel (0–255) can be shown as a 3x3 matrix as another way to describe it.
 Next, the matrix's centre value utilizes to verify the threshold.
 This value employs to define new values of 8 neighbours.
 A new binary value will be established for every neighbour of the threshold value. The threshold was set at 1, with 0 denoting values below it, and 1
designating value equal to or higher than the threshold.
 The matrix only consisted of binary values. The concatenation of every binary value is done from every place in the matrix line by line for producing a
novel binary value. When several researchers combine multiple the binary values in diverse ways, no change is found in outcome.
 Thereafter, a central value of the matrix, a pixel from the original image, is set to this binary value subsequent to its transformation to a decimal value.
 When this procedure is completed, a novel image is obtained that leads to capture the qualities of the actual picture in more precise way.
LBP (Local Binary Pattern) Algorithm

Pseudo
code
of
Voting
Classification

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