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By IBM Developer Staff | Published September 21, 2018
Artificial IntelligenceVisionFacial RecognitionImage Feature Extraction
The model detects faces in an input image and then generates an embedding vector for each face. The generated embeddings can be used for downstream tasks such as classification, clustering, verification etc. The model accepts an image as input and returns the bounding box coordinates, probability and embedding vector for each face detected in the image. The model is based on the the FaceNet model.
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-facial-recognizer
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Facial-Recognizer/master/max-facial-recognizer.yaml
Once deployed, you can test the model from the command line. For example if running locally:
curl -F "image=@assets/Lenna.jpg" -XPOST http://localhost:5000/model/predict
You should see a JSON response like that below:
Use computer vision, TensorFlow, and Keras for image classification and processing.
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