Are you Hot or Not?
Let Artificial Intelligence decide if you are Hot or Not
The Idea under Attractiveness AI
Often people wonder, "how attractive am I?" or “am I hot?” or "am I hot or not?”
We have all asked ourselves these questions, however until Hotness.ai, a person had limited options in determining how attractive they were. Typically, the only options available were simple facial attractiveness tests that used either no facial recognition software or appeared to respond with random scores. While these facial attractiveness tests were largely for entertainment purposes, the overall experience left people wanting something more accurate.
Hotness.ai offers anybody the ability to have their photo scanned by facial recognition software and compared against a database of other photos.You just have to choose the photo of yourself that you wish to upload to the Facial Attractiveness Test, which will then scan the photo to determine the person’s facial features based on a number of different facial points. A facial attractiveness score between 1 and 10 is then displayed under the person’s photo.
Facial Features Recognition
The facial recognition api developed by Haystack.ai determines the person's facial features by mapping their face. The shape and size of the eyes, nose, cheekbones, mouth and jaw are some of the important features used in determining a person's unique facial structure. The facial recognition software also determines a person's age based on a variety of features.
There are important factors in regards to facial attractiveness that determine how a facial attractiveness score is calculated once a person’s facial points have been determined. For a person’s eyes, the distance between the eyes and the depth of the eye sockets are important factors. Important factors for a person’s nose, the width of the nose and the length of the nose. Other important factors include the size of a person’s lips, the length, and width of their chin and jaw and the position of their cheeks.
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 Hotness.ai Facial Attractiveness Test, this new information also helps determine a more accurate facial attractiveness 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 attractiveness 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 attractiveness. Deep learning also is used to improve the Hotness.ai Facial Attractiveness Test scores by comparing previous facial features and their facial attractiveness scores with new photos to form a scoring curve of more and more accurate facial attractiveness scores.
How Old Are You?
Let Artificial Intelligence tell how old are you ,try out our other app which will tell you, how old you look.
Ethnicity & Diversity Recognition
Let Artificial Intelligence tell your ethincity by doing diversity recognition ,try out this other app which will tell your ethinicty. It's based on Haystack Ethnicity Recognition API.
The Hotness.ai mobile app offers a person the ability to upload their photos from their mobile phones and tablets to have their facial attractiveness calculated and scored. The Hotness.ai mobile app also provides users the ability to anonymously rate other users' facial attractiveness, using the same scoring system of 1 to 10. These user scores are then fed into deep learning to help the facial recognition api determine the attractiveness curve based on current trends in the way real people view the facial attractiveness of others.
These facial features and facial attractiveness scores are calculated together and compared against a database of other facial features and facial attractiveness scores to determine a current facial attractiveness score. The result is a more accurate facial attractiveness score between 1 and 10, with 1 being low facial attractiveness and 10 being high facial attractiveness, based on the previous and current facial features and facial attractiveness scores.