Compare Amazon Rekognition and Azure Face API 
- Users report that Amazon Rekognition excels in its Quality of Support, with a score of 9.1, while Azure Face API falls short with a score of 6.9. Reviewers mention that the responsiveness and helpfulness of Amazon's support team significantly enhance the user experience.
- Reviewers say that Amazon Rekognition has a higher overall G2 rating of 4.3 compared to Azure Face API's 3.8, indicating a stronger overall satisfaction among users. Users on G2 highlight that Rekognition's features are more robust and user-friendly.
- Users report that Amazon Rekognition shines in its Emotion Detection capabilities, which are noted for their accuracy and reliability. In contrast, Azure Face API's Emotion Detection is mentioned as less comprehensive, with some users expressing disappointment in its performance.
- G2 users mention that Amazon Rekognition's ease of setup is rated at 9.1, making it more accessible for small businesses, while Azure Face API, with a score of 9.3, is also easy to set up but lacks the same level of user guidance that Rekognition provides.
- Reviewers mention that Amazon Rekognition offers superior Facial Analysis features, with users praising its detailed insights and accuracy. Azure Face API, while competent, is noted to have limitations in the depth of analysis it provides.
- Users say that Amazon Rekognition's deployment options are more flexible, allowing for easier integration into various applications, whereas Azure Face API is reported to have a more rigid integration process, which can be a hurdle for some users.
I like the fact that Amazon Rekognition is very efficient in recognizing the elements in photos or videos without extensive training. It is a handy tool if we don't want to go to the roots of the Computer Vision models and just focus on the implementation.
It can become overwhelming at times with all the options but for the most part its a trial and error learning process.
The idea that it could detect a person's age and gender is interesting in itself, but the aspect I find most intriguing is how it can detect human emotions. Using this Face API can be beneficial to many companies.
That it doesn't have all the functions needed. Sometimes it would just not work and freeze on me. Also, it can be a bit buggy and I would have to find a different solution to it.
I like the fact that Amazon Rekognition is very efficient in recognizing the elements in photos or videos without extensive training. It is a handy tool if we don't want to go to the roots of the Computer Vision models and just focus on the implementation.
The idea that it could detect a person's age and gender is interesting in itself, but the aspect I find most intriguing is how it can detect human emotions. Using this Face API can be beneficial to many companies.
It can become overwhelming at times with all the options but for the most part its a trial and error learning process.
That it doesn't have all the functions needed. Sometimes it would just not work and freeze on me. Also, it can be a bit buggy and I would have to find a different solution to it.
