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AI Blitz XIII: Completed #educational #blitz
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Problem Statements

 

๐Ÿ““Starter Kit for Beginners | ๐Ÿ‘ฏโ€โ™€๏ธ Find Your Teammate | ๐Ÿ† Community Contribution Prize | ๐ŸŽง Discord AI Community

๐Ÿ‘‹ Introduction

Each face has a story to tell. Our faces allow us to communicate without using any words. A human face is not just a gateway to the world but a provider of information. By looking at someoneโ€™s face, you can identify their emotions, age, familiarity and more. Is there a way we can extract this helpful information without human interference?

Face recognition technology was seen as something out of science fiction until recently. But the technological advances in the past few years have made it viable and widespread. The fast-growing field of facial recognition has several real-world applications ranging from banking, law enforcement, biometrics, retails and more. One of the most common uses is the Face Unlock that you might be using on your phones. Like any technology, face recognition is not free of controversy. The technology can be used to aid forensics, make our devices safer, or help solve cases of missing persons. Yet it can also be used for purposes such as mass surveillance. But face recognition is here to stay, and the first way to ensure its appropriate use is to understand the technology behind it. So letโ€™s go!

๐Ÿ’ก Did You Know โ€ฆ

The earliest research in Facial Recognition technology dates back to the 1960s? Woody Bledsoe, Helen Chan Wolf and Charles Bisson were pioneers of the field, but much of their work was never published due to circumstances. It was later found that their initial work involved the manual marking of various โ€œlandmarksโ€ on the face; eyes, mouth, hairline, nose, etc. They developed a system of classifying photos of faces by hand using the RAND table. This system used the coordinates location of various facial features and imputed these details into a database. When a new photo was given, it could retrieve an image from the database that most closely resembled that individual. Their innovation was limited by the technology of the time. Still, they inspired many others who applied linear algebra to the problem of facial recognition and feature analysis, known as an Eigenface. With the growth of social media and access to many images, tech companies like Facebook, Google, and phone developers like Apple could develop practical facial recognition applications. 

WHAT IS AI BLITZ 13 โšก๏ธ FACES ALL ABOUT?

This Blitz brings you the essential computer vision AI problems around face recognition. Solving these 5 Blitz puzzles will prepare you to tackle more advanced face recognition problems in AI. โ€‹โ€‹Backed by easy-to-understand starter kits and active support from AIcrowd Community, make your first submission in 15-minutes! In classic Blitz tradition, leaderboard toppers and community contributors stand a chance to win from a cash prize pool of $400! What are you waiting for? Start solving AI Blitz 13 puzzles so find out what lies behind these faces. 

  1. Sentiment Classification: Our eyes can identify any emotions by looking at a face, but can we train an AI model to do that?
  2. Age Prediction: Built a model to guess the approximate age of a person given their image. 
  3. Mask Prediction: Can we predict the mask type and bounding box from just an image? 
  4. Face Recognition: In the sea of faces, find the target face using your model. 
  5. Face De-blurring: No more blurry selfies, create a model to get clear images. 

 

๐Ÿ“… TIMELINE

  • Start Time: 4th February 2022 | 12:00 PM UTC
  • End Time: 25th February 2022 | 12:00 PM UTC
  • Duration: 21 days/3 weeks

๐Ÿ’ป INSTRUCTIONS

  • Each problem has an associated weight. This weight denotes the problemโ€™s contribution to your final score.
  • The final leaderboard is calculated by the weighted mean of your rank across all the problems in the contest. If a problem is not attempted by the participant, his/her rank in that problem is denoted by the total number of participants in the whole challenge.
  • To help you get started we have a starter kit available for each of these problems. We hope they are helpful; if you find any bugs, typos, or improvements, please send us a pull request.
  • In case you have any queries, please reach out to us via the Discussion Forums.

๐Ÿ† PRIZES

  • Leaderboard 1st Place: $100 
  • Leaderboard 2nd Place: $100
  • Community Contribution:  2 x $100

๐Ÿงพ ELIGIBILITY

AI BlitzXIIIโšก is open to everyone who is interested in diving into the world of Data sciences - students, professionals, or researchers. With problems of varying difficulty, we try to ensure that there is something for everyone. For eligibility on prizes please read the rules of the challenge.

Problem Setter: Aditya Jha, Shubhamai, Divyanshu Kumar, Sharada Mohanty, Ayush Shivani

Team: Aditya JhaShubhamaiAkanksha Tyagi, Gauransh KumarSneha Nanavati, Ayush Shivani, Sharada Mohanty, Rabiul Islam

Interested in helping us out or want to put your own puzzle in the next iteration of this competition? Please send an email to ashivani@aicrowd.com.

Interested in sponsoring AI Blitz in the next iteration of this competition? Please send an email to mohanty@aicrowd.com.

๐Ÿ“ฑ CONTACT

If you have any questions, consider posting on the Blitz 13 Community Discussion board, or join the party on our Discord!

Participants

Leaderboard

01
420.000
02
  GLaDOS
430.000
03 bdlHq_mwqwf 520.000
04
545.000
05
  Cufix
585.000

Notebooks

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Face Age Prediction using Transfer Learning [81.3 F1]
By
hemanth_kollipara
4 months ago
0
Face Mask Detection using Detecto Pytorch 98.1 AP
By
hemanth_kollipara
4 months ago
0