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What Role Does Data Science Play in Facial Recognition?

 

 

By analyzing images of human faces, a facial recognition tool creates numerical representations that may be compared to others to help identify a particular individual. Facial recognition is altering businesses by serving a variety of objectives, from enabling checkout-free shopping at retail establishments to finding missing or abused victims of human trafficking.

How Data Science Is used in Facial Recognition:

  1.  Identifying Human Faces

In order to identify a match, the facial recognition system analyses every characteristic of the face and compares it to its database.

  1.  Detecting Objects

In addition to cameras and AI software, computers recognize images using machine vision technology. Visual search is one of the most sophisticated techniques in data science since picture recognition is used to carry out many machine-based visual activities, such as searching for images based on their content and labeling images with meta-data.

To know more in depth about object detection, check out the artificial intelligence course in Canada, designed by tech leaders. 

  1. Recognizing Patterns

By using its database, data science can match any unique patterns in an image, such as facial expressions or textures. It also can recognize shapes and colors present in the image, giving users accurate information about the image's contents.

 

Top technology for Facial Recognition:

  • Deep Vision AI

It uses powerful computer vision technology to recognize video and photos automatically, transforming visual input into insightful data and real-time analytics. Deep Vision AI allows us to analyze camera streams using different AI-based technologies on a single plug-and-play platform, giving users faster responsiveness and real-time alerts. There are already over 500M cameras in use worldwide.

  • SenseTime

With the help of big data analytics and AI advancements, SenseTime is committed to producing business solutions. In addition to image recognition, facial identification, autonomous driving, intelligent video analytics, and medical image recognition, SenseTime's multipurpose technology is fast expanding.

 Its platform software includes:

  • Amazon Rekognition

Rekognition is a cloud-based SAAS computer vision platform by Amazon that makes it simple to add a picture and video analysis to apps using tested and highly scalable deep learning technology that requires no machine learning expertise to utilize. With the help of this platform, one may recognize individuals, things, settings, text, and actions in images and videos, as well as any objectionable material. Additionally, it enables incredibly precise facial analysis and facial search characteristics that may be used to recognize, assess, and verify faces in various consumer verification, population-counting, and public safety scenarios.

  • FaceFirst

FaceFirst software seeks to provide excellent customer experiences while fostering safer communities and secure transactions. Businesses use their computer vision technology for automated video analytics and facial identification to deter crime and increase customer engagement in retail, transportation, event, and other industries.

  • Trueface

People may interpret the data from their cameras and turn it into useful information by using the industry-leading business-computer vision model True Face. Nevertheless, it exclusively provides its partners with on-premise computer vision solutions that increase data security and processing speed. The platform-agnostic solutions have received particular training to function in various ecosystems. Assuring equitable functionality for all racial and gender groups maintains the highest priority on the diversity of training data.

  • Face++

It is an open AI platform with computer vision technologies that let software programmes better understand the outside world. With its robust APIs and SDKs, individuals can easily include cutting-edge, deep learning-based image analysis and recognition technologies into their apps. Face++ analyses 106 data points on the face, identifies faces, and uses AI and machine vision in various fantastic ways to authenticate a person's identification with a high degree of detail.

 

I hope you found this post interesting in your quest as a data scientist.To jumpstart your career, sign up for a data science course in Canada, and work on 15+ real-world projects from diverse domain electives.






 
Publication: 22 September 9:53

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