@exadel/compreface-js-sdk

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

CompreFace JS SDK version	CompreFace 0.4.x	CompreFace 0.5.x	CompreFace 0.6.x	CompreFace 1.0.x
0.4.1	✔	✘	✘	✘
0.5.x	✘	✔	:yellow_circle:	:yellow_circle:
0.6.x	✘	:yellow_circle:	✔	:yellow_circle:
1.0.x	✘	:yellow_circle:	:yellow_circle:	✔

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).

Enviroments	from URL	with Blob format	from local machine
Browser	✔	✔	✘
Nodejs	✔	✔	✔

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)

Argument	Type	Required	Notes
url	string	required	URL with protocol where CompreFace is located. E.g. http://localhost
port	string	required	CompreFace port. E.g. 8000
options	object	optional	Default values for face recognition services

Possible options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
prediction_count	integer	maximum number of subject predictions per face. It returns the most similar subjects. Default value: 1
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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.

Argument	Type	Required	Notes
api_key	string	required	Face Recognition Api Key in UUID format

Example:

let recognitionService = compreFace.initFaceRecognitionService(api_key);

1. compreFace.initFaceDetectionService(api_key)

Inits face detection service object.

Argument	Type	Required	Notes
api_key	string	required	Face Detection Api Key in UUID format

Example:

let detectionService = compreFace.initFaceDetectionService(api_key);

1. compreFace.initFaceVerificationService(api_key)

Inits face verification service object.

Argument	Type	Required	Notes
api_key	string	required	Face Verification Api Key in UUID format

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.

Argument	Type	Required	Notes
image_location	string	required	URL, image in BLOB format or image from your local machine
options	string	optional	Object that defines recognition options

Supported options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
prediction_count	object	maximum number of subject predictions per face. It returns the most similar subjects. Default value: 1
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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"
 }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
subjects	list	list of similar subjects with size of <prediction_count> order by similarity
similarity	float	similarity that on that image predicted person
subject	string	name of the subject in Face Collection
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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.

Argument	Type	Required	Notes
image_location	string	required	URL, image in BLOB format or image from your local machine
subject	string	required	Name or any other person ID. It can be just a random string you generate and save for further identification
options	string	optional	Object that defines adding options

Supported options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0

Response:

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

Field	string	Notes
image_id	string	ID of the saved image
subject	string	Name or any other person ID

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"
    }
  ]
}

Field	string	Notes
image_id	string	ID of the saved image
subject	string	Name or any other person ID

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.

Argument	Type	Required	Notes
subject	string	optional	Name or any other person ID. If empty deletes all images in the face collection

Response:

{
    "deleted": <count>
}

Element	Type	Description
deleted	integer	Number of deleted faces

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.

Argument	Type	Required	Notes
image_id	string	required	ID of the saved image

Response:

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

Field	string	Notes
image_id	string	ID of the deleted image
subject	string	Name or any other person ID

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.

Argument	Type	Required	Notes
image_ids	string[]	required	IDs of the saved images to delete

Response:

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

Field	string	Notes
image_id	string	ID of the deleted image
subject	string	Name or any other person ID

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.

Argument	Type	Required	Notes
image_location	string	required	URL, image in BLOB format or image from your local machine
options	string	optional	Object that defines recognition options

Supported options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
prediction_count	object	maximum number of subject predictions per face. It returns the most similar subjects. Default value: 1
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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"
  }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
similarity	float	similarity that on that image predicted person
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject. It can be any string

Response:

{
  "subject": "subject1"
}

Element	Type	Description
subject	string	is the name of the subject

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

List Subjects

Returns all subject related to Face Collection.

subjects.list()

Response:

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

Element	Type	Description
subjects	array	the list of subjects in Face Collection

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)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject that will be updated
new_name	string	required	is the name of the subject. It can be any string

Response:

{
  "updated": "true|false"
}

Element	Type	Description
updated	boolean	failed or success

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)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject.

Response:

{
  "subject": "subject1"
}

Element	Type	Description
subject	string	is the name of the subject

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>"
}

Element	Type	Description
deleted	integer	number of deleted subjects

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.

Argument	Type	Required	Notes
image_location	string	required	URL, image in BLOB format or image from your local machine
options	string	optional	Object that defines detection options

Supported options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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"
  }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face (on processedImage)
probability	float	probability that a found face is actually a face (on processedImage)
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face (on processedImage)
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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.

Argument	Type	Required	Notes
source_image_location	string	required	URL, source image in BLOB format or source image from your local machine
target_image_location	string	required	URL, target image in BLOB format or target image from your local machine
options	string	optional	Object that defines detection options

Supported options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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"
    }
  }]
}

Element	Type	Description
source_image_face	object	additional info about source image face
face_matches	array	result of face verification
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
similarity	float	similarity between this face and the face on the source image
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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.