Artificial intelligence fortune-telling prediction

Speaking of ai face recognition fortune telling, everyone knows that someone asked if ai face monitoring for more than 20 points was hopeless. In addition, some people want to ask how to adjust the ai ? ? of face recognition to return multiple desired results at once, such as age, gender, race and emotion. Do you know what this is about? In fact, what are the routines for fortune telling with AI technology? Let's take a look at more than 20 ai face monitoring face value scores. Is there no hope? I hope I can help you!

Artificial intelligence face recognition fortune telling

1, ai face recognition fortune telling: ai face monitoring value of more than 20 points is hopeless? The so-called face value score is just playing games. You don't have to take it seriously, it can only be roughly the same. Because people's face value is not confirmed by the machine. As the saying goes, beauty is in the eye of the beholder, and judging a person's face value is multifaceted, not determined by appearance.

There are many factors that affect face recognition, among which the factors that affect face detection are: illumination, face posture and occlusion degree; The factors that affect feature extraction are illumination, expression, occlusion and age, and blur is the key factor that affects the accuracy of face recognition. However, there are more influencing factors in cross-age face detection.

Generally speaking, in cross-age face recognition, intra-class variation is usually greater than inter-class variation (the similarity of photos of different people with similar ages is sometimes higher than that of photos of the same person with different ages), which makes face recognition extremely difficult. At the same time, it is difficult to collect cross-age training data. Without enough data, it is difficult for neural networks based on deep learning to learn intra-class and inter-class changes across ages.

What are the routines for fortune telling with AI technology? Related introduction:

Hairstyle requires not only the front work, but also the preparation of the side work. There are generally three kinds of sides: convex side, flat side and concave side.

Features: small forehead, big nose, strong sense of outline, and a face with ethnic characteristics.

Scheme: When you do hairstyle with this face, you should first increase the hair on your forehead to make your face look straight, and the hair on the back of your head can also be increased appropriately, but you should pay attention to the discretion, otherwise it will be self-defeating. Free AI intelligence.

Too rich curly hair will only make the outline of this face look stronger. This face is more suitable for long curly hair (micro-curl).

Features: the lateral line of the face is too straight, with little fluctuation.

Scheme: The relative hairstyle of this face is forbidden to have straight hair, and the application of curly hair can alleviate the straightness of the side lines of your face, and the curly hair can be exaggerated, while the neat curly hair is full of beautiful hairstyles with wild beauty.

Features: Its features are opposite to convex side. Its most striking feature is a prominent chin.

Solution: How to turn the disadvantage of chin into an advantage? Free artificial intelligence visit photos.

Pay attention not to have too much hair in the front jaw, and use soft edges and swelling in the back of the head. At the same time, it also makes the chin that hates protruding and protruding suddenly become a lot sexier.

2. How does the ai that adjusts face recognition return multiple desired results at once, such as age, gender, race and emotion? face recognition technology

First of all, let's learn about the technology of face recognition itself. With the continuous evolution of artificial intelligence technology, the accuracy of face recognition is gradually improving. We can already see the news that many companies have set new records on the authoritative face recognition database LFW, and the data in the laboratory is as high as 99.5% or even higher. This is the basis of applying face recognition technology to practical business, and we are also happy about it.

There are many factors that affect face recognition, among which the factors that affect face detection are: illumination, face posture and occlusion degree; The factors that affect feature extraction are illumination, expression, occlusion and age, and blur is the key factor that affects the accuracy of face recognition. However, there are more influencing factors in cross-age face detection.

Generally speaking, in cross-age face recognition, intra-class variation is usually greater than inter-class variation (the similarity of photos of different people with similar ages is sometimes higher than that of photos of the same person with different ages), which makes face recognition extremely difficult. At the same time, it is difficult to collect cross-age training data. Without enough data, it is difficult for neural networks based on deep learning to learn intra-class and inter-class changes across ages. Sweep your face and tell your fortune.

In view of these technical difficulties, at present, relevant technology providers are seeking breakthroughs by optimizing algorithms and increasing model training. We can also learn about the development progress of face recognition monitoring accuracy from relevant materials, and their landing fields include the most widely used security, finance, business and other applications.

The above is more than 20 points of face monitoring with ai. Is there no hope? Related content, ai face monitoring is hopeless for more than 20 points? Share. I saw ai face recognition fortune telling, I hope this will help everyone!