Crowdsourcing

The process of collecting and processing information with the help of a wide circle of volunteers. This method is used for various tasks related to artificial intelligence and machine learning where a large amount of diverse data is required

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DATA COLLECTION

Selfie ID

Photographs of people's faces using selfies to create a facial biometrics database. These data aid in developing facial recognition systems

Speech biometrics

Recording voice samples from different people to create a database for voice recognition, including various languages, accents, and intonations

Unconventional biometrics

Photographs of people with various external changes: baldness, makeup, beards, glasses, etc. This enhances the accuracy of face recognition under diverse conditions

Images

A broad spectrum of images including different objects, scenes, and situations, to improve computer vision algorithm

Replay attacks

Data simulating attempts to bypass security systems, such as photos, video recordings, or audio, for testing and improving security systems

Video

Recording videos with different objects in various lighting and environmental conditions to help algorithms better understand and interpret diverse visual data

Audio

Recording diverse audio data, including speech, music, environmental noises, etc., for training algorithms in recognizing and analyzing audio

Documents

Collecting images of various documents, such as driver's licenses, passports, etc., to create a database for use in document recognition and verification systems

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DATA labeling

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Identification and classification of objects, faces, actions, and scenarios in images or videos. Involves object highlighting, face recognition, emotion detection, as well as annotation of movements and interactions

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Advantages of Crowdsourcing

Scalability of Projects:

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Using crowdsourcing for data collection is more cost-effective. Crowd workers typically work on a pay-per-task basis, which is generally more economical than hiring and maintaining full-time employees
Free pilot

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AI models usually perform more efficiently with large and diverse datasets. Crowdsourcing offers scalability, which can often be challenging for internal teams
Diversity:

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The demographics of data collectors significantly impact the detail and diversity of your dataset. Access to global crowd workers with varied experiences and demographics allows crowdsourcing to avoid the complexities of creating international teams from different regions
Increased Accuracy:

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A broader range of data participants can lead to more reliable and trustworthy datasets. Such a large-scale approach to data collection reduces errors and improves data quality, contributing to the development of more accurate AI models