Facial Age Estimator

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Overview

This model detects faces in an image, extracts facial features for each face detected and finally predicts the age of each face. The model uses a coarse-to-fine strategy to perform multi-class classification and regression for age estimation. The input to the model is an image and the output is a list of estimated ages and bounding box coordinates of each face detected in the image. The format of the bounding box coordinates is [ymin, xmin, ymax, xmax], where each coordinate is normalized by the height and width of the image dimension for y and x, respectively. Each coordinate is therefore in the range [0, 1]. The model is based on the SSR-Net model.

Model Metadata

Domain Application Industry Framework Training Data Input Data Format
Vision Facial Detection General Keras & TensorFlow IMDB-WIKI Dataset Image (PNG/JPG)

References

Licenses

Component License Link
This repository Apache 2.0 LICENSE
Model Weights MIT LICENSE
Model Code (3rd party) MIT LICENSE
Test assets Various Samples README

Options available for deploying this model

This model can be deployed using the following mechanisms:

  • Deploy from Dockerhub:
docker run -it -p 5000:5000 codait/max-facial-age-estimator
  • Deploy on Kubernetes:
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Facial-Age-Estimator/master/max-facial-age-estimator.yaml

Example Usage

You can test or use this model

Test the model using cURL

Once deployed, you can test the model from the command line. For example if running locally:

curl -F "image=@samples/tom_cruise.jpg" -XPOST http://localhost:5000/model/predict
{
    "status": "ok",
    "predictions": [
        {
            "age_estimation": 47,
            "detection_box": [
                0.3079667063020214,
                0.147,
                0.6813317479191439,
                0.562
            ]
        }
    ]
}

Test the model in a Node-RED flow

Complete the node-red-contrib-model-asset-exchange module setup instructions and import the facial-age-estimator getting started flow.

Test the model in CodePen

Learn how to send an image to the model and how to render the results in CodePen.