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Let Artificial Intelligence tell your ethnicity & diversion
The Idea under Ancestry AI
Ethnicity Recognition by AI functions on a very innovative technology. Our app successfully combines the Artificial Intelligence technology with deep learning, which allows the system to constantly collect information and to improve its services based on that. When it comes to the diversity recognition algorithms, every new piece of information helps our system evolve and makes it deliver even more accurate results.
Our photo ethnicity analyzer is fed up with new information on a daily basis, making sure that it will identify even the most complex ethnicities, without making any confusion. The deep learning technology is even more intriguing, as the system always perfects itself, reviewing the previously analyzed photos and learning from its past mistakes. By confronting the past and the current results, the system is able to establish its margin of error, fix it and apply this for every new analyzed photo.
Mobile Support
Ethnicity Recognition by Artificial Intelligence is also available on mobile devices, and now you can upload your photos to your tablet/smartphone and establish your ethnicities. The mobile friendliness will find its use in plenty of social situations since you can take a photo of your friends/family members and show them their ethnicity results.
Deep Learning
Deep learning offers a variety of benefits to artificial intelligence algorithms. Essentially, it is the process of continually feeding new information into an artificial intelligence system and increasing the amount of information in the databases used for many purposes, including mapping the history of and guiding the predictions of an artificial intelligence system. For facial recognition systems, this new information is used to evolve the artificial intelligence algorithms that help determine accurate facial points. In the case of the Ancestry.ai Facial ethnicity & diversion detection Test, this new information also helps determine a more accurate facial ethnicity & diversion detection score.
New data is constantly fed into deep learning, which uses existing and new data to identify facial features better and more accurately determine a facial ethnicity & diversion detection score, is an important part in the development of better accuracy and scoring.
Deep learning is used to continually increase the accuracy of the facial recognition process by comparing new photos of a person’s face with a continually growing database of photos previously evaluated for facial ethnicity & diversion detection. Deep learning also is used to improve the Ancestry.ai Ethnicity Detection Confidance scores by comparing previous facial features and their facial ethnicity & diversion detection scores with new photos to form a scoring curve of more and more accurate facial ethnicity & diversion detection scores.
Mobile App
It's very simple to establish your ethnicity when you have a professional app like Ethnicity Recognition. You will always have fun using it for you or for your friends, but you can always download photos from the internet and find out the ethnicity of certain celebrities. Since artificial intelligence is quickly becoming a standard technology, why not benefit from it in a fun but educative way? Don't waste time with mapping family trees or with performing expensive biologic tests. Download Ethnicity Recognition by Artificial Intelligence from Google Play, and get ready to find your true ethnicities, in a matter of seconds.
These facial features and facial ethnicity & diversion detection scores are calculated together and compared against a database of other facial features and facial ethnicity & diversion detection scores to determine a current facial ethnicity & diversion detection score. The result is a more accurate facial ethnicity & diversion detection score between 1 and 10, with 1 being low facial ethnicity & diversion detection and 10 being high facial ethnicity & diversion detection, based on the previous and current facial features and facial ethnicity & diversion detection scores.