The everything about Biometric facial recognition you may be to need to know
Author: huifan Time: 2021-02-22
The Internet likes buzzwords: artificial intelligence, machine learning, biotechnology, natural language processing, neural networks, gene editing, robotic process automation and collective intelligence, but little is known about the actual meaning of these terms or how it will affect their lives .
Source: "Business Insider"
If your position is a chief whisperer or a resident hacker, you may have some ideas, but for most technological advances, they are either confused and scared, or confused and excited.
One technology that I am particularly excited about is access control facial recognition and analysis. With the support of artificial intelligence (especially machine learning), this technology has existed for decades. As computing power and machine learning algorithms improve, facial recognition and its application methods have also improved.
Biometric facial recognition is a system used to recognize people from images or videos. More precisely, recognition software can recognize unique facial and physical characterstics and convert them into unique IDs, which are then used to link unique characteristics to users and/or user information.
Although Woodrow Wilson Bledsoe conducted the first experiments with semi-automatic computer-based facial recognition in the mid-1960s, it has only become more popular in recent years. It is possible that you are already using this technology without even realizing it. For example, if you use your face to unlock your phone or personal photo application to automatically identify people who often appear in your photos, you can "tag you".
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2. How it works
The smart face recognition analysis function allows users to understand the location of faces in an image or video and the attributes of these faces. For example, the software analyzes attributes such as how far apart the eyes are, what color the eyes are, what the mood is, what color the hair is, and the visual geometry of the face.
These tools work by providing a confidence score that is correct in their hypothesis. In other words, they predict the accuracy of what they believe to be their assumptions. Machine learning algorithms are trained on a dataset of hundreds of thousands of images and continue to improve as more data is added and more faces are analyzed.
It is understandable that there are many uneasiness and worries about the way the technology is developed and how it will be used in the future. For this reason, it is important to eliminate some common misunderstandings.
First of all, as you might think, machines are better at recognizing faces than humans. The National Institute of Standards and Technology (NIST) recently shared a study on facial recognition technology and concluded that even older technologies may outperform human facial recognition capabilities. Unbelievable, but it is true.
Although we hope that everyone will be biased to a certain extent, one of the biggest concerns and driving factors of using facial recognition technology is that the technology itself is biased in identifying age, race and even gender and taking action. It will be adopted based on the results of the algorithm. Or not. This inherent bias may be a direct result of the data on which the algorithm was trained.
In order for facial recognition technology to achieve the desired results (accurate and fair), training data must provide sufficient balance and coverage. The training data set should be large enough and diverse enough to learn many different ways of facing the world. In other words, the image must reflect the diversity of global facial features. Therefore, it is crucial that the creators and implementers of this technology plan and review the representation in the data.
Fortunately, some excellent plans are underway. For example, IMB is creating a face diversity (DiF) set of millions of images to ensure that creators can actually use a sufficiently large and diverse data set. As with any AI system, this algorithm is only as good as the data on which it was constructed and trained.
Second, as in all probability systems, the mere presence of false positives does not mean that facial recognition is flawed. Instead, it emphasizes that best practices must be followed, such as setting reasonable confidence score thresholds related to a given use case.
For example, Amazon Web Services, the creator of AWS Rekognition, recommends that when law enforcement officers use facial recognition technology for identification or in a way that might threaten civil liberties, a 99% confidence score threshold should be applied. Therefore, depending on how and why the technology is used, the confidence score should be set accordingly. Think of the confidence score as a measure of how much a facial recognition system trusts its results; the higher the confidence score, the more trustworthy the result.
One of the main advantages of this technology is that it is constantly learning and improving, so over time, false positives can and will decrease.
Use cases for facial recognition equipment are endless. From using the GMCNgine™ of the International Center for Missing and Exploited Children (ICMEC) to find missing children, to checking in at your local gymnasium, to paying for your next cheeseburger by smiling at the camera, the application of this technology The way is advancing by leaps and bounds.
Some of the latest and more innovative examples include MasterCard identity checks and Alibaba's "Pay with a Smile". For people living in West Africa, Aella Credit uses biometric data and employer data to provide instant loans to individuals with verifiable sources of income in emerging markets. Even tourist destinations are using this technology. Now, in Wuzhen, China, tourists are required to upload selfies to visit key areas of the historic town. This technology shortens waiting time and increases convenience for guests. read more.
From art to graphics to influential marketing to healthcare, identification technology is being used in more and more clever ways.
5. Face the future
New technology can be daunting and usually unknown, but due to its potential abuse, it should not be prohibited or condemned. On the contrary, there should be an open, honest and serious dialogue between the parties concerned to ensure proper application and continuous strengthening of the dialogue. For those who use and actively build facial recognition tools, we are all responsible for applying context restrictions to the system we develop to mitigate harm,For those who actively use and build facial recognition tools, we are all responsible for applying these restrictions to our development system to reduce the harm in the system.
Pay close attention to what the market leaders are doing. For example, Microsoft plans to implement self-designed ethical principles for its facial recognition technology by the end of March 2018, and urges governments to promote matching regulations in this area. Similarly, AWS is publishing articles and lobbying the government on how to use facial recognition responsibly. In places where video surveillance (including facial recognition) is used, basic knowledge such as not infringing on individual rights (including privacy and written notices) should be unreasonable.
What do you think you can and should use this technology in? Share your thoughts with us and let us continue to learn and debate.
6. HFSecurity biometric solution provider
HFSecurity's biometric solution provider leads HFSecurity's cloud innovation department. Their product, HFSecurity, facial recognition, fingerprint scanner, and access control, is a cloud-based security alternative to global reception desks, which can replace access control and paper-based ancient sign-in systems.
If you have any questions about biometrics, you can contact us and we will have professional staff to answer you. Let us discuss progress together and contribute our strength to the development of biometrics
About HFSecurity Biometric Solution Provider
Huifan Technology successfully customized a variety of requirements for facial recognition temperature for customers