Comparison of two face recognition software: Clarifai and Face++

Recently, I tried several products to extract demographic information from a profile image. My target was to obtain information about age, gender, and ethnicity. I found the prominent companies in the sector are Clarifai and Face++. I integrated my trial software with both products and I found Clarifai’s accuracy better than Face++. My reasons are:

  1. Clarifai provides the probability value of its predictions. (predicted gender is female with a probability %52) So, it is possible to eliminate the results having low prediction score. On the contrast, Face++ does not provide that value. This is an unwanted situation because, in binary classification technique, the prediction always has a result, even its score is not very high.
  2. Clarifai correctly predicted the ethnicity of the image below as “White”, while Face++ wrongly predicted it as “Black”. But on the other hand, Clarifai could not found the gender value correctly (female %51, male %49) while Face++ correctly marked it as male (we don’t know its probability).
  3. The disadvantage of Clarifai is its low quota for free usages. It permits only 2500 API calls per month for free accounts. But Face++ does not specify any upper limit for free accounts. It has only one single limitation, which is one single API call per second.

I hope my hands-on experience with these services will help you choose the right product.

 

 

Result of Clarifai: (https://clarifai.com/demo)

Gender: feminine (prob. score: 0.510), masculine(prob. score: 0.490)
Age: 55 (prob. score: 0.356)
Ethnicity (Multicultural appearance):  White: (prob. score: 0.981)

Result of Face++: (https://www.faceplusplus.com/attributes/#demo)

Gender: male
Age: 53
Ethnicity (Multicultural appearance): Black

Converting texts to high-res images

A very inspiring research is made at the end of 2016. With the help of deep learning, now it is possible to generate images from given texts.

Here is the link to the news and here is the link to that research paper.

Could you imagine some use cases based on this technology? I found an interesting use case.. Imagine you are in a police station, about a robbery occurred in a bank… The thief could not be found and you explain the visual profile of thief as you are the unique eyewitness of this event. At that time, a computer automatically generates the image of thief based on the visual details you describe… At the same time, the computer increases the precision of that visual by matching it with other records of past robbery events.

The Future of Education

Within the last month, the future of education was one of the main topics in Davos. There were very interesting debates, and in of them, Jack Ma (the founder of Alibaba) told that it is strongly and urgently needed to change the current education system due to the rising impact of robots. Since robots are able to obtain the knowledge, by learning from their past experiences, they will do most of the things people do today. In order to adapt ourselves to the modern world, we need to educate our children in a way that cannot be copied by robots. Rather than teaching mathematics or physics to our children, we should support their more humanistic skills such as music and art.

I agree with Jack Ma’s ideas and I think we need to think more about people’s main advantages and disadvantages over robots in the next 20 years. Today, our children start learning to code in primary school, in order to communicate better with the robots and understand their logic. But when the world will be dominated by robot activities, all the things will be changed and humans should be in a place where robots do not see them as a threat.