face-detect

List of contents

• What is face-detect?
• Installation

What is face-detect?

• Face-Detect detects human faces in an image using the Face REST API and Python.
• A successful response is returned is JSON.

For example, consider the following image:

The response for this image will be as follows:

[
  {
    "faceId": "e93e0db1-036e-4819-b5b6-4f39e0f73509",
    "faceRectangle": {
      "top": 621,
      "left": 616,
      "width": 195,
      "height": 195
    },
    "faceAttributes": {
      "smile": 0,
      "headPose": {
        "pitch": 0,
        "roll": 6.8,
        "yaw": 3.7
      },
      "gender": "male",
      "age": 37,
      "facialHair": {
        "moustache": 0.4,
        "beard": 0.4,
        "sideburns": 0.1
      },
      "glasses": "NoGlasses",
      "emotion": {
        "anger": 0,
        "contempt": 0,
        "disgust": 0,
        "fear": 0,
        "happiness": 0,
        "neutral": 0.999,
        "sadness": 0.001,
        "surprise": 0
      },
      "blur": {
        "blurLevel": "high",
        "value": 0.89
      },
      "exposure": {
        "exposureLevel": "goodExposure",
        "value": 0.51
      },
      "noise": {
        "noiseLevel": "medium",
        "value": 0.59
      },
      "makeup": {
        "eyeMakeup": true,
        "lipMakeup": false
      },
      "accessories": [],
      "occlusion": {
        "foreheadOccluded": false,
        "eyeOccluded": false,
        "mouthOccluded": false
      },
      "hair": {
        "bald": 0.04,
        "invisible": false,
        "hairColor": [
          {
            "color": "black",
            "confidence": 0.98
          },
          {
            "color": "brown",
            "confidence": 0.87
          },
          {
            "color": "gray",
            "confidence": 0.85
          },
          {
            "color": "other",
            "confidence": 0.25
          },
          {
            "color": "blond",
            "confidence": 0.07
          },
          {
            "color": "red",
            "confidence": 0.02
          }
        ]
      }
    }
  },
  {
    "faceId": "37c7c4bc-fda3-4d8d-94e8-b85b8deaf878",
    "faceRectangle": {
      "top": 693,
      "left": 1503,
      "width": 180,
      "height": 180
    },
    "faceAttributes": {
      "smile": 0.003,
      "headPose": {
        "pitch": 0,
        "roll": 2,
        "yaw": -2.2
      },
      "gender": "female",
      "age": 56,
      "facialHair": {
        "moustache": 0,
        "beard": 0,
        "sideburns": 0
      },
      "glasses": "NoGlasses",
      "emotion": {
        "anger": 0,
        "contempt": 0.001,
        "disgust": 0,
        "fear": 0,
        "happiness": 0.003,
        "neutral": 0.984,
        "sadness": 0.011,
        "surprise": 0
      },
      "blur": {
        "blurLevel": "high",
        "value": 0.83
      },
      "exposure": {
        "exposureLevel": "goodExposure",
        "value": 0.41
      },
      "noise": {
        "noiseLevel": "high",
        "value": 0.76
      },
      "makeup": {
        "eyeMakeup": false,
        "lipMakeup": false
      },
      "accessories": [],
      "occlusion": {
        "foreheadOccluded": false,
        "eyeOccluded": false,
        "mouthOccluded": false
      },
      "hair": {
        "bald": 0.06,
        "invisible": false,
        "hairColor": [
          {
            "color": "black",
            "confidence": 0.99
          },
          {
            "color": "gray",
            "confidence": 0.89
          },
          {
            "color": "other",
            "confidence": 0.64
          },
          {
            "color": "brown",
            "confidence": 0.34
          },
          {
            "color": "blond",
            "confidence": 0.07
          },
          {
            "color": "red",
            "confidence": 0.03
          }
        ]
      }
    }
  }
]

Installation

• Clone the project by running the following command: git clone https://github.com/hmangukia/face-detect.git
• For setting up the project, you will need a valid Azure subscription for KEY and Face API URL.
• Then you will need to create a resource for using Azure's cognitive services. You can do it by following this tutorial.
• After getting the keys, set the values of KEY and API.
• Set the url(image_url) of the image you want to perform detection on.
• To run the project, open terminal in your project folder and run the following command: python3 detect-face.py

Successful JSON response will be given in the terminal showing various features like age, gender, facial hair, emotion, makeup(eye/lip), accessories, hairs(color/bald or not) and more.