2. Automatic facial detection
based on the machine learning
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Department of Computer Science and Engineering
LINGAYAS INSTITUTE OF MANAGEMENT AND TECHNOLOGY
Guidance of Presented by
Mr. Rama Krishna Sayyad Althaf
5. Artificial Intelligence
Artificial Intelligence is a method of making a computer, a
computer-controlled robot, or a software think intelligently like
the human mind.
AI is accomplished by studying the patterns of the human
brain and by analyzing the cognitive process. The outcome of
these studies develops intelligent software and systems.
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6. Machine learning
Machine learning is a branch of artificial intelligence
(AI) and computer science which focuses on the use
of data and algorithms to imitate the way that humans
learn, gradually improving its accuracy.
Smartphones use personal voice
assistants like Siri, Alexa, Cortana, etc. These
personal assistants are an example of ML-based
speech recognition that uses Natural Language
Processing to interact with the users and
formulate a response accordingly.
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7. Computer vision
Computer vision is an exciting space in machine learning
Computer vision machine enables to identify people , places and things in images
with accuracy at or human level
The image data can take many forms such as images, video sequences, views from
multiple cameras or three dimensional data
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9. Facial recognition
Face recognition systems are part of facial image processing applications and their
significance
as a research area are increasing recently. They use biometric information of humans
and are
applicable easily instead of fingerprint, iris, signature etc., because these types of
biometrics are
not much suitable for non-collaborative people. Face recognition systems are usually
applied
and preferred for people and security cameras in metropolitan life. These systems can
be used
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10. OBJECTIVE
The objective of face recognition is, from the incoming image, to find
a series of data of the same face in a set of training images in a
database.
The great difficulty is ensuring that this process is carried out in real-
time, something that is not available to all biometric face recognition
software providers.
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11. Advantages
Improved Public Security
Fast and Non-Invasive Identity Verification
Benefits of Facial Recognition in Banking
Better Worker Attendance Systems
Benefits of Facial Recognition in Retail
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12. Disadvantages
1. The face recognition system can’t tell the difference between
identical twins.
2. Affected by the environment.
3. Facial recognition can be expensive.
4. Privacy concerns related to facial recognition.
5. High false o match range.
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14. Implementation 14
Facial Recognition is a category of biometric software that maps an
individual’s facial features and stores the data as a face print.
The software uses deep learning algorithms to compare a live captured
image to the stored face print to verify one’s identity. Image processing
and machine learning are the backbones of this technology.
15. Face recognition algorithms
Eigenfaces
Fisherfaces
Eigenfaces:
Eigenfaces is a face recognition algorithm, which uses principal
component analysis(PCA). PCA is a statistical approach that is used for
dimensionality reduction. Eigenfaces reduce some less important features
from the image and take only important and necessary features of the
image. Eigenfaces reduce dimensionality with having important features of
the image.
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16. Fisher faces:
Fisher faces is also a face recognition algorithm. In the Fisher faces
algorithm, we only find the feature that distinguishes one from the other for
that we are using linear discriminant analysis(LDA). LDA is used for class
separability and dimensionality reduction by a linear combination of
features. Fisherfaces use features that distinguish one from others instead
of using the common feature.
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18. Conclusion
Conclusion Face recognition is an emerging technology that can provide
many benefits. Face recognition can save resources and time, and even
generate new income streams, for companies that implement it right.
Faces change dramatically with development, but the influence of
change with development on algorithm performance could not be
examined..
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19. Future Scope
The Future of Face Recognition Today, one of the fields that uses facial
recognition the most is security.
Facial recognition is a very effective tool that can help law enforcers recognize
criminals and software companies are leveraging the technology to help users
access their technology.
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