Face API - Facial Recognition-Microsoft Azure Cognitive Services Showing different confidence score

Mohammed Fazil 21 Reputation points
2021-06-27T08:20:11.743+00:00

Face verification
Check the likelihood that two faces belong to the same person and receive a confidence score.
face

![109589-image.png]3

Confidence return - 0.93

When we are comparing the two faces belong to the same person using the same Face API - Facial Recognition - Microsoft Azure Cognitive Services we got a different confidence score.

109599-image.png

Confidence return - 0.83

Please check the above result and please tell me what i have to do
i tried all the logic that i can use but did not get any same results

So experts please help me

Azure Face
Azure Face
An Azure service that provides artificial intelligence algorithms that detect, recognize, and analyze human faces in images.
154 questions
Azure AI services
Azure AI services
A group of Azure services, SDKs, and APIs designed to make apps more intelligent, engaging, and discoverable.
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Accepted answer
  1. romungi-MSFT 42,321 Reputation points Microsoft Employee
    2021-06-28T08:25:21.027+00:00

    @Mohammed Fazil The result of the response depends on the detection and recognition models used for the images when they are uploaded using face detect. Usually the latest versions of these have a better accuracy i.e recognition_04 and detection_03 models and these models could be optimized further based on improvements to the backend algorithms.

    The demo page above is displaying a sample which simplifies the scenario by using a certain set of parameters or values to create face ids using Face Detect and then verifying them using Face Verify. The score seen is also from this response. With the latest version of these models I receive a score of 0.94087 which is quite high and they are identical.

    {  
      "isIdentical": true,  
      "confidence": 0.94087  
    }  
    

    I would recommend to use these models and not compare the score with demo site as this is periodically updated based on documentation changes. If you are using older detection and recognition models I would recommend to update the same for better accuracy. I hope this helps.


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