The Knowledge of Face Recognition
- Author:HFSecurity
- Release on:2020-09-15
Facial recognition technology has developed rapidly in just a few years of the 21st
century. According to the research and test of the National Institute of Standards
and Technology (NIST), the deadline is April 2020. The error rate of the facial
recognition algorithm It is 0.08%. This error rate is when this algorithm is the best
face recognition algorithm. Under the same conditions, the facial recognition algorithm
in 2014 was only 4.1%. Enough to see the development speed of facial recognition.
What is biometric facial recognition?
Face recognition technology is based on the facial features of a person, and the input
face image or video stream is first judged whether there is a face, if there is a face,
then the position, size and main points of each face are further given. Location information
of facial organs. Based on this information, the identity features contained in each face are
further extracted and compared with known faces to identify the identity of each face.
The broad sense of face recognition android actually includes a series of related technologies
for building a face recognition system, including face image collection, face positioning,
face recognition preprocessing, identity confirmation, and identity search, etc.; while the
narrow sense of face recognition machine specifically refers to the adoption of A
technology or system for identifying or searching for human faces.
The biological characteristics studied by biometric recognition technology include face,
fingerprint, palm print, iris, retina, voice (voice), body shape, personal habits (such as
the strength and frequency of typing on the keyboard, signature), etc. The corresponding
recognition technology has people Face recognition, fingerprint recognition, palmprint
recognition, iris recognition, retina recognition, voice recognition (voice recognition can
be used for identity recognition and voice content recognition, only the former belongs
to biometric recognition technology), body shape recognition, keyboard strokes
Identification, signature recognition, etc.
Best Face recognition software is to compare the facial features to be
recognized with the obtained facial feature template, and judge the identity
information of the face based on the degree of similarity. This process is
divided into two categories: one is confirmation, which is a process of one-to-one
image comparison, and the other is identification, which is a process of one-to-many
image matching and comparison.
Face image collection and detection
Face image collection: Different face images can be collected through the
camera lens, such as static images, dynamic images, different positions,
different expressions, etc. can be well collected. When the user is within
the shooting range of the capture device, the capture device will automatically
search for and shoot the user's face image.
Face detection: In practice, face detection is mainly used for preprocessing
of AI face recognition, that is, to accurately calibrate the position and size
of the face in the image. The pattern features contained in face images are
very rich, such as histogram features, color features, template features,
structural features, and Haar features. Face detection is to pick out the
useful information, and use these features to realize face detection.
How accurate is facial recognition?
The visual artificial intelligence industry is driven by technology, and the
core of the technology lies in the three aspects of data, computing power,
and algorithms. The emergence of GPU and AI dedicated chips has broken
through the computing power bottleneck of traditional CPUs, and data
computing speed and processing scale have exploded. Big data analysis
provides hardware support. More and more application fields are accumulating
increasingly rich big data. Massive image and video content provide powerful
data support for deep learning.
The emergence of deep learning has greatly promoted the development of the
visual artificial intelligence industry. In 2015, in the Imagenet competition
of the visual artificial intelligence system recognition project, Resnet surpassed
the 5.19% of human vision for the first time with a recognition error rate of
3.57%. At present, the accuracy of 3d face recognition has been improved to
over 97%.
At present, the application of visual artificial intelligence on a global scale is
concentrated in the fields of smart consumption and smart manufacturing, with
remarkable results and the continuous expansion of subdivisions. With the
continuous development of technology, visual artificial intelligence can recognize
the types of information from the initial text information to the recognition of
human faces, human postures, and various objects. The recognition accuracy has
also changed from the initial 1:1 comparison to the 1:N comparison used in access
control systems, etc., and the M:N dynamic monitoring used in blacklist monitoring
and other scenarios. At the same time, the automation of data labeling has been
greatly improved. Further improve identification efficiency and reduce identification
costs.
Due to the popularity of deep learning, machine learning research has flourished
in recent years, and real time face recognition technology has also been greatly
improved. In a typical use case, faces in photos, videos or real-time streaming media
are scanned and analyzed, and then their features are compared with the annotated
faces in the database.
How to ensure the accuracy of face recognition Device?
This technology is being used to combat human trafficking and rapid airport security
checks, while it is also being used to monitor concerts and sports events.
However, the accuracy of facial recognition is still an issue. Researchers are beginning
to worry about discrimination and prejudice in artificial intelligence systems. The
technology still has major flaws in correctly identifying people of color and women.
One of the reasons for this problem is the disparity in the proportion of men relative
to women and whites relative to colored people in the data set.
For machine training, data diversity is important, but the size of the data is also
important. The training and testing of face recognition systems need to be carried
out on millions or even tens of millions of faces.
For many years, researchers have been conducting related research through face
recognition security system data sets. This data set containing image links is organized
and generated from a resource pack. This resource pack is used for various scientific
project research, including research on estimating the location of photos and videos
without using geographic coordinates.
IBM has developed a new project called "Face Diversity" and prepared millions of
pictures for it. This project will further improve the fairness and accuracy of face
recognition.
Researchers collected and annotated photos of various objects through the Internet
to train computers to better understand the world around them.
Usually, they get a huge number of pictures through Google image search and some
other ways. The resulting data set is usually used for academic research, such as
training or testing face recognition algorithms. But with companies such as
Microsoft, Amazon, Facebook and Google betting on artificial intelligence, facial
recognition is moving out of the laboratory and entering the field of vision of
large companies.
As consumers realize the immense power of the data they leave on the Internet,
face recognition data sets are exacerbating concerns about privacy and surveillance.
Therefore, some researchers are re-examining this brutal collection of photos of
others. In the Internet full of sharing spirit, the use of other people's photos
should have their consent.
Many companies, research institutions, and individuals have compiled data sets for
facial recognition, and IBM is just one of them. Some of these data sets are
composed of actual images, and there are some IBM-like data sets that are
composed of image links. Sometimes, the data set can also be obtained by shooting
models.
Normally, these facial recognitiondata sets are knowledge-sharing, but they must
be used for non-commercial purposes, such as algorithm research. But a large number
of similar data sets can be downloaded for free from the website.
The facial recognition knowledge sharing agreement was first released in 2002,
far before the current artificial intelligence boom.
Although the researchers use pictures for free on the website, they also
admit that many people who upload these pictures may be surprised by
the fact that the pictures are used to train artificial intelligence.
Some china facial recognition researchers believe that people should decide
for themselves whether images can be used for computer vision or artificial
intelligence research through authorization.
In this regard, the face recognitionknowledge sharing agreement does not
help much. As long as the relevant terms are followed, this license agreement
from a non-profit organization does not restrict any form of artificial intelligence
development.
The CEO of Creative Commons stated: "These agreements are not designed
to protect privacy or research ethics."
In recent years, artificial face recognition intelligence has developed so fast
that the relevant regulations have hardly been formulated, let alone implemented.
Legally, the company has no obligation to inform when collecting and using images for
facial recognition.
No relevant facial recognitiion federal regulations have been issued yet. In each state, the
situation is different: For example, Illinois has a law that requires companies
to obtain customer consent before collecting biometric information; the state
Senate in Washington, where Amazon and Microsoft are headquartered, recently
passed a restriction on facial expressions. To identify the use of the bill, the
bill still needs to be passed by the State House of Representatives.
In March of this year, the Senate introduced a bill requiring companies to
obtain consumer consent before collecting and sharing identification data. It
also requires the company to conduct external tests to ensure that the
algorithm is fair before implementation.
The facial recognition technical policy director of the Electronic Frontier
Foundation, a digital copyright organization, said that even if there are no strict
laws restricting the use of private photos for artificial intelligence training, companies and
research groups should pay attention to ethics.
In his view, Facial Recognition device this means that the use of photos requires the explicit consent
of the people in the photos. Even if this is difficult to do, it is a reality that
companies must face.
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