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April 28, 2021

How Data Science Is Used In Facial Recognition| Data Science Daily | Episode 23

Facial recognition is an application that generates numerical representations by analyzing pictures of human faces, to compare against other human faces to verify a person’s identity. From checkout-free at retail stores to identifying missing or exploited victims of human trafficking, facial recognition is changing industries by serving various purposes.

Deep Vision AI

It applies sophisticated computer vision technology to know video and images automatically, changing visual content into valuable insights and real-time analytics. With over 500M existing cameras across the world, Deep Vision AI enables us with the ability to analyze camera streams through various AI-based software on a single plug-and-play platform, enabling users with faster responsiveness and real-time alerts.

 

SenseTime

SenseTime is focused on generating business solutions via innovations in AI and big data analytics. SenseTime’s multifunctional technology is quickly growing and already includes image recognition, facial recognition, autonomous driving, intelligent video analytics, and medical image recognition.

 its platform software includes:  

•  SensePortrait-S •  SensePortrait D

  

Amazon Rekognition

Rekognition is a cloud-based SAAS computer vision platform by Amazon, which makes it simple to add a picture and video analysis to applications utilizing proven and highly scalable, deep learning tech that needs no machine learning knowledge to use. This platform enables one to identify, people, objects, scenes, text, and activities in pictures and videos, also identify any inappropriate content. It also allows for extremely accurate facial analysis and facial search features that can be utilized to identify, analyze, and check faces for a number of consumer verification, people counting, and public safety situations.

  

FaceFirst

The FaceFirst software aims to create safer communities and secure transactions while providing great buyer experiences. Its computer vision platform is utilized by businesses for facial recognition and automated video analytics to enable retailers, transportation centers, event venues, and other businesses to prevent crime and boost consumer engagement.

 

Trueface

True Face is a leading business-computer vision model that enables people to make sense of their camera info and transform it into actionable information. Although It offers only on-premise computer vision solutions that enhance data security and performance speeds for its partners. The platform-agnostic solutions are specially trained to work in a variety of ecosystems. It keeps the highest priority on the diversity of training data ensuring equal functionality for all ethnicities and genders.

  

Face++

 It is an AI Open Platform that has computer vision technologies that allow people’s applications to comprehend the world better. It enables people to simply add leading, deep learning-based image analysis recognition technologies into their applications, with powerful APIs and SDKs.

 Face++ uses AI and machine vision in so many amazing ways to identify faces, analyze 106 data points on the face, and confirm an individual’s identity with a high degree of specificity.

 

How data science is used in facial recognition:

 

1.  Identifying Human Faces

Huge quantity of digital data is being used by businesses to deliver better services to their customers. The facial recognition system interprets all the features of the face and compares it with its database to find a match.

 

2.  Detecting Objects

Computers use machine vision technologies along with a camera and AI software to get image recognition. Since picture recognition is leveraged to act upon a lot of machine-based visual tasks, from labeling the info of pictures with meta-tags to performing picture content search; visual search is one of the most advanced tools of data science.

 

3.  Recognizing Patterns

data science is effective at recognizing any exclusive patterns, whether it is facial expressions or texture, in the picture and matches it with its database. Additionally, It has the potential to detect shapes and colors present in the picture and provides the users with proper insight into the contents of the picture.

 

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