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CompreFace JavaScript SDK

CompreFace JavaScript SDK makes face recognition into your application even easier.

Table of content

• Requirements
• Installation
• Usage
  • Initialization
  • Adding faces into a face collection
  • Recognition
  • Environments
  • Webcam demo
• Reference
  • CompreFace Global Object
    • Methods
  • Recognition Service
    • Recognize Faces from a Given Image
    • Get Face Collection
      • Add an Example of a Subject
      • List of All Saved Examples of the Subject
      • Delete All Examples of the Subject by Name
      • Delete an Example of the Subject by ID
      • Verify Faces from a Given Image
    • Get Subjects
      • Add a Subject
      • List Subjects
      • Rename a Subject
      • Delete a Subject
      • Delete All Subjects
  • Face Detection Service
    • Detect
  • Face Verification Service
    • Verify
• Contributing
  • Report Bugs
  • Submit Feedback
• License info

Requirements

Before using our SDK make sure you have installed CompreFace and Nodejs on your machine.

1. CompreFace (See below compatibility matrix)
2. Nodejs (Version 10+)

CompreFace compatibility matrix

Explanation:

• ✔ SDK supports all functionality from CompreFace.
• :yellow_circle: SDK works with this CompreFace version. In case if CompreFace version is newer - SDK won't support new features of CompreFace. In case if CompreFace version is older - new SDK features will fail.
• ✘ There are major backward compatibility issues. It is not recommended to use these versions together

Installation

To add CompreFace JS SDK to your project, run the following command in the project folder:

npm i @exadel/compreface-js-sdk

Usage

Initialization

To start using JavaScript SDK you need to import CompreFace object from 'compreface-js-sdk' dependency.

Then you need to init it with url and port. By default, if you run CompreFace on your local machine, it's http://localhost and 8000 respectively. You can pass optional options object when creating CompreFace to set default parameters, see reference for more information.

After you initialized CompreFace object you need to init the service object with the api key of your face service. You can use this service object to recognize faces.

However, before recognizing you need first to add faces into the face collection. To do this, get the face collection object from the service object.

import { CompreFace } from 'compreface-js-sdk';

let api_key = "your_key";
let url = "http://localhost";
let port = 8000;

let compreFace = new CompreFace(url, port); // set CompreFace url and port 
let recognitionService = compreFace.initFaceRecognitionService(api_key); // initialize service
let faceCollection = recognitionService.getFaceCollection(); // use face collection to fill it with known faces
let subjects = recognitionService.getSubjects(); // use subjects object to work with subjects directely

Adding faces into a face collection

Here is JavaScript code example that shows how to add an image to your face collection from your file system:

let path_to_image = "../images/boy.jpg";
let name = encodeURIComponent('Tom');

faceCollection.add(path_to_image, name)
    .then(response => {
        // your code
    })
    .catch(error => {
        console.log(`Oops! There is problem in uploading image ${error}`)
    })

Recognition

This code snippet shows how to recognize unknown face:

let path_to_image = "../images/team.jpg";

recognitionService.recognize(path_to_image)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem with recognizing image ${error}`)
    })

Environments

NOTE: We provide 3 ways of uploading image to our SDK. They are url, blob and relative path (from local machine).

Webcam demo

Documentation is here

Reference

CompreFace Global Object

Global CompreFace Object is used for initializing connection to CompreFace and setting default values for options. Default values will be used in every service method if applicable. If the option’s value is set in the global object and passed as a function argument then the function argument value will be used.

Constructor:

new CompreFace(server, port, options)

Possible options:

Example:

let server = "http://localhost";
let port = 8000;
let options = {
  limit: 0, 
  det_prob_threshold: 0.8, 
  prediction_count: 1,
  face_plugins: "calculator,age,gender,landmarks",
  status: "true"
}

let compreFace = new CompreFace(server, port, options);

Methods

1. compreFace.initFaceRecognitionService(api_key)

Inits face recognition service object.

Example:

let recognitionService = compreFace.initFaceRecognitionService(api_key);

2. compreFace.initFaceDetectionService(api_key)

Inits face detection service object.

Example:

let detectionService = compreFace.initFaceDetectionService(api_key);

3. compreFace.initFaceVerificationService(api_key)

Inits face verification service object.

Example:

let verificationService = compreFace.initFaceVerificationService(api_key);

Recognition Service

Face recognition service is used for face identification. This means that you first need to upload known faces to face collection and then recognize unknown faces among them. When you upload an unknown face, the service returns the most similar faces to it. Also, face recognition service supports verify endpoint to check if this person from face collection is the correct one. For more information, see CompreFace page.

Recognize Faces from a Given Image

recognitionService.recognize(image_location, options)

Recognizes all faces from the image. The first argument is the image location, it could be a URL or a path on the local machine.

Supported options:

Response:

{
 "result" : [ {
   "age" : {
     "probability": 0.9308982491493225,
     "high": 32,
     "low": 25
   },
   "gender" : {
     "probability": 0.9898611307144165,
     "value": "female"
   },
   "mask" : {
     "probability": 0.9999470710754395,
     "value": "without_mask"
   },
   "embedding" : [ 9.424854069948196E-4, "...", -0.011415496468544006 ],
   "box" : {
     "probability" : 1.0,
     "x_max" : 1420,
     "y_max" : 1368,
     "x_min" : 548,
     "y_min" : 295
   },
   "landmarks" : [ [ 814, 713 ], [ 1104, 829 ], [ 832, 937 ], [ 704, 1030 ], [ 1017, 1133 ] ],
   "subjects" : [ {
     "similarity" : 0.97858,
     "subject" : "subject1"
   } ],
   "execution_time" : {
     "age" : 28.0,
     "gender" : 26.0,
     "detector" : 117.0,
     "calculator" : 45.0,
     "mask": 36.0
   }
 } ],
 "plugins_versions" : {
   "age" : "agegender.AgeDetector",
   "gender" : "agegender.GenderDetector",
   "detector" : "facenet.FaceDetector",
   "calculator" : "facenet.Calculator",
   "mask": "facemask.MaskDetector"
 }
}

Example:

let image_location = "../images/team.jpg";
let options = {
    limit: 0,
    det_prob_threshold: 0.8,
    prediction_count: 1,
    face_plugins: "calculator,age,gender,landmarks",
    status: "true"
}

recognitionService.recognize(image_location, options)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem with recognizing image ${error}`)
    })

Get Face Collection

recognitionService.getFaceCollection()

Returns Face collection object

Face collection could be used to manage known faces, e.g. add, list, or delete them.

Face recognition is performed for the saved known faces in face collection, so before using the recognize method you need to save at least one face into the face collection.

More information about face collection and managing examples here

Methods:

Add an Example of a Subject

faceCollection.add(image_location, subject, options)

Adds an image to your face collection.

Supported options:

Response:

{
 "image_id": "string",
 "subject": "string"
}

Example:

let image_location = "../images/boy.jpg";
let name = encodeURIComponent('Tom');
let options = {
    det_prob_threshold: 0.8
}

faceCollection.add(image_location, name, options)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem in uploading image ${error}`)
    })

List of All Saved Examples of the Subject

faceCollection.list()

Retrieve a list of images saved in a Face Collection

Response:

{
  "faces": [
    {
      "image_id": "string",
      "subject": "string"
    }
  ]
}

Example:

faceCollection.list()
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem: ${error}`)
    })

Delete All Examples of the Subject by Name

faceCollection.delete_all_subject(subject)

Removes image(s) according to their given subject.

Response:

{
    "deleted": <count>
}

Example:

let subject = "Tom";

faceCollection.delete(subject)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem ${error}`)
    })

Delete an Example of the Subject by ID

faceCollection.delete(image_id)

Remove image from face collection.

Response:

{
 "image_id": "string",
 "subject": "string"
}

Example:

let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";

faceCollection.delete(image_id)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem ${error}`)
    })

Delete Multiple Examples

faceCollection.delete_multiple_images(image_ids)

Remove images from face collection.

Response:

{
 "image_id": "string",
 "subject": "string"
}

Example:

let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";

faceCollection.delete(image_id)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem ${error}`)
    })

Verify Faces from a Given Image

faceCollection.verify(image_path, image_id, options)

Compares similarities of given image with image from your face collection.

Supported options:

Response:

{
  "result" : [ {
    "age" : {
      "probability": 0.9308982491493225,
      "high": 32,
      "low": 25
    },
    "gender" : {
      "probability": 0.9898611307144165,
      "value": "female"
    },
    "mask" : {
      "probability": 0.9999470710754395,
      "value": "without_mask"
    },
    "embedding" : [ 9.424854069948196E-4, "...", -0.011415496468544006 ],
    "box" : {
      "probability" : 1.0,
      "x_max" : 1420,
      "y_max" : 1368,
      "x_min" : 548,
      "y_min" : 295
    },
    "landmarks" : [ [ 814, 713 ], [ 1104, 829 ], [ 832, 937 ], [ 704, 1030 ], [ 1017, 1133 ] ],
    "subjects" : [ {
      "similarity" : 0.97858,
      "subject" : "subject1"
    } ],
    "execution_time" : {
      "age" : 28.0,
      "gender" : 26.0,
      "detector" : 117.0,
      "calculator" : 45.0,
      "mask": 36.0
    }
  } ],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator",
    "mask": "facemask.MaskDetector"
  }
}

let image_location = "../images/team.jpg";
let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";
let options = {
    limit: 0,
    det_prob_threshold: 0.8,
    prediction_count: 1,
    face_plugins: "calculator,age,gender,landmarks,mask",
    status: "true"
}

faceCollection.verify(image_location, image_id, options)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem with verifying image ${error}`)
    })

Get Subjects

recognitionService.getSubjects()

Returns subjects object

Subjects object allows working with subjects directly (not via subject examples).

More information about subjects here

let subjects = recognitionService.getSubjects(); 

Methods:

Add a Subject

Create a new subject in Face Collection.

subjects.add(subject)

Response:

{
  "subject": "subject1"
}

let subjects = recognitionService.getSubjects();
subjects.add("John");

List Subjects

Returns all subject related to Face Collection.

subjects.list()

Response:

{
  "subjects": [
    "<subject_name1>",
    "<subject_name2>"
  ]
}

let subjects = recognitionService.getSubjects();
console.log(subjects.list());

Rename a Subject

Rename existing subject. If a new subject name already exists, subjects are merged - all faces from the old subject name are reassigned to the subject with the new name, old subject removed.

subjects.rename(subject, new_name)

Response:

{
  "updated": "true|false"
}

let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.rename("John", "Jane");
console.log(subjects.list());

Delete a Subject

Delete existing subject and all saved faces.

subjects.delete(subject)

Response:

{
  "subject": "subject1"
}

let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.delete("John");
console.log(subjects.list());

Delete All Subjects

Delete all existing subjects and all saved faces.

subjects.deleteAll()

Response:

{
  "deleted": "<count>"
}

let subjects = recognitionService.getSubjects();
subjects.add("John");
subjects.add("Jane");
console.log(subjects.list());
subjects.deleteAll();
console.log(subjects.list());

Face Detection Service

Face detection service is used for detecting faces in the image.

Methods:

Detect

detectionService.detect(image_location, options)

Finds all faces on the image. The first argument is the image location, it could be a URL or a path on the local machine.

Supported options:

Response:

{
  "result" : [ {
    "age" : {
      "probability": 0.9308982491493225,
      "high": 32,
      "low": 25
    },
    "gender" : {
      "probability": 0.9898611307144165,
      "value": "female"
    },
    "mask" : {
      "probability": 0.9999470710754395,
      "value": "without_mask"
    },
    "embedding" : [ -0.03027934394776821, "...", -0.05117142200469971 ],
    "box" : {
      "probability" : 0.9987509250640869,
      "x_max" : 376,
      "y_max" : 479,
      "x_min" : 68,
      "y_min" : 77
    },
    "landmarks" : [ [ 156, 245 ], [ 277, 253 ], [ 202, 311 ], [ 148, 358 ], [ 274, 365 ] ],
    "execution_time" : {
      "age" : 30.0,
      "gender" : 26.0,
      "detector" : 130.0,
      "calculator" : 49.0,
      "mask": 36.0
    }
  } ],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator",
    "mask": "facemask.MaskDetector"
  }
}

Example:

let image_location = "../images/team.jpg";
let options = {
    limit: 0,
    det_prob_threshold: 0.8,
    face_plugins: "calculator,age,gender,landmarks",
    status: "true"
}

detectionService.detect(image_location, options)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem with recognizing image ${error}`)
    })

Face Verification Service

Face verification service is used for comparing two images. A source image should contain only one face which will be compared to all faces on the target image.

Methods:

Verify

verificationService.verify(source_image_location, target_image_location, options)

Compares two images provided in arguments. Source image should contain only one face, it will be compared to all faces in the target image. The first two arguments are the image location, it could be a URL or a path on the local machine.

Supported options:

Response:

{
  "result" : [{
    "source_image_face" : {
      "age" : {
        "probability": 0.9308982491493225,
        "high": 32,
        "low": 25
      },
      "gender" : {
        "probability": 0.9898611307144165,
        "value": "female"
      },
      "mask" : {
        "probability": 0.9999470710754395,
        "value": "without_mask"
      },
      "embedding" : [ -0.0010271212086081505, "...", -0.008746841922402382 ],
      "box" : {
        "probability" : 0.9997453093528748,
        "x_max" : 205,
        "y_max" : 167,
        "x_min" : 48,
        "y_min" : 0
      },
      "landmarks" : [ [ 92, 44 ], [ 130, 68 ], [ 71, 76 ], [ 60, 104 ], [ 95, 125 ] ],
      "execution_time" : {
        "age" : 85.0,
        "gender" : 51.0,
        "detector" : 67.0,
        "calculator" : 116.0,
        "mask": 36.0
      }
    },
    "face_matches": [
      {
        "age" : {
          "probability": 0.9308982491493225,
          "high": 32,
          "low": 25
        },
        "gender" : {
          "probability": 0.9898611307144165,
          "value": "female"
        },
        "mask" : {
          "probability": 0.9999470710754395,
          "value": "without_mask"
        },
        "embedding" : [ -0.049007344990968704, "...", -0.01753818802535534 ],
        "box" : {
          "probability" : 0.99975,
          "x_max" : 308,
          "y_max" : 180,
          "x_min" : 235,
          "y_min" : 98
        },
        "landmarks" : [ [ 260, 129 ], [ 273, 127 ], [ 258, 136 ], [ 257, 150 ], [ 269, 148 ] ],
        "similarity" : 0.97858,
        "execution_time" : {
          "age" : 59.0,
          "gender" : 30.0,
          "detector" : 177.0,
          "calculator" : 70.0,
          "mask": 36.0
        }
      }],
    "plugins_versions" : {
      "age" : "agegender.AgeDetector",
      "gender" : "agegender.GenderDetector",
      "detector" : "facenet.FaceDetector",
      "calculator" : "facenet.Calculator",
      "mask": "facemask.MaskDetector"
    }
  }]
}

Example:

let source_image_location = "../images/boy.jpg";
let target_image_location = "../images/team.jpg";
let options = {
    limit: 0,
    det_prob_threshold: 0.8,
    face_plugins: "calculator,age,gender,landmarks",
    status: "true"
}

verificationService.verify(source_image_location, target_image_location, options)
    .then(response => {
        console.log(JSON.stringify(response));
    })
    .catch(error => {
        console.log(`Oops! There is problem with recognizing image ${error}`)
    })

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

After creating your first contributing pull request, you will receive a request to sign our Contributor License Agreement by commenting your pull request with a special message.

Report Bugs

Please report any bugs here.

If you are reporting a bug, please specify:

• Your operating system name and version
• Any details about your local setup that might be helpful in troubleshooting
• Detailed steps to reproduce the bug

Submit Feedback

The best way to send us feedback is to file an issue at https://github.com/exadel-inc/compreface-javascript-sdk/issues.

If you are proposing a feature, please:

• Explain in detail how it should work.
• Keep the scope as narrow as possible to make it easier to implement.

License info

CompreFace JS SDK is open-source facial recognition SDK released under the Apache 2.0 license.