'Building a Serverless Lie Detector that Uses Facial Recognition AI' artificialintelligence serverless
In this case, we can either hardcode the name or get it from the HTTP trigger function body.will indicate to us that this HTTP triggered function runs successfully by outputting thefunc start
In this final part, we will explore Azure Cognitive Services, integrate the Face API to analyze emotions, and use the Courier API to send alerts when specific deceiving emotions are detected.“Create a Resource” and locate “AI + Machine Learning” in the list of categories on the left. { "IsEncrypted": false, "Values": { "AzureWebJobsStorage": "", "FUNCTIONS_WORKER_RUNTIME": "node", "API_KEY": "replace-with-your-key", "FACE_API_KEY": "replace-with-your-azure-key", "FACE_ENDPOINT": "replace-with-your-azure-endpoint" } }Just as we used the Courier SDK, we will use the Face service with Azure’s SDK, which can be found on thein the terminal to install the Face...
async function analyze_face { const faceKey=process.env["FACE_API_KEY"]; const faceEndPoint=process.env["FACE_ENDPOINT"]; const cognitiveServiceCredentials=new CognitiveServicesCredentials; const client=new FaceClient; const url= "https://www.allkpop.com/upload/2021/12/content/231225/web_data/allkpop_1640280755_untitled-1.jpg"; const options={ returnFaceAttributes: ["emotions"] }; client.face .detectWithUrl .then; console.log; }) .catch; console.
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