The FACEIO development team is pleased to announce the immediate availability of our Liveness Detection, Presentation Attacks Mitigation (PAD) & Face Anti-Spoofing Security Option. A highly requested feature by our customers that is now deployed & fully operational on FACEIO thanks to a PixLab implementation.

Liveness detection in biometrics is the ability for a system to detect if the target face is real from a live person present at the point of capture or fake from a spoof artifact or lifeless body part.

Face Anti-Spoofing on the other side, is the task of preventing false facial authentication when implementing fio.js, our facial recognition library on your web site or application using a smartphone picture, or printed image for an authorized person's face.

Face Anti-Spoofing Security Option

With this security option enabled, Smartphone & Print Attacks originating from a static image or video stream are easily deterred during Authentication & Enrollment when enabled for your FACEIO application.

The Face Anti-Spoofing Security Option is immediately available to trigger from the Application Manager (Security Tab) on the FACEIO Console.

Press Release & Model Roadmap

Our Press Release document including a general overview of the approach used to implement Face Anti-Spoofing for FACEIO is available to consult at: cdn.faceio.net/faceio_liveness_detection.pdf.

This implementation & further model enhancements, are pushed transparently, and no action or code changes are required on your side. Just an option to trigger from the Application Manager on the FACEIO CONSOLE.

Implementation Overview

Once a new user enroll or authenticate on your application, the Face Anti-Spoof engine shall be triggered to filter out spoof & presentation attacks. Upon an attack is detected, the fioErrCode.PAD_ATTACK error code is raised, and you should act accordingly such as banning the acting user depending on your policy.

Please note that Face Anti-Spoofing mitigation is a resource intensive operation, therefore it is available starting from the Business Plan, and up. Nevertheless, it takes less than 600 milliseconds to execute thanks to a heavily optimized implementation.

The PixLab development team is pleased to announce the immediate availability of FACEIO's fio.js V 1.9. A major release which implements highly requested features from our customers including but not limited to:

• Face Duplication (same user enrolling twice or more) Prevention.
• Dynamic Session Reloading without Page Refreshing.
• Faster facial features collection during enrollment.
• Upto 1.3x faster facial authentication operation.
• French, Japanese, Vietnamese & Dutch new language packs.

As usual, this update is transparent to our end users & fio.js implementers, and no action is required on your side if you link directly the library from the FACEIO CDN.

Face Duplication Prevention:

When enabled, this option shall prevent the same user from enrolling twice or more on your application. That is, if the facial features of the user being enrolled are already recorded, the enroll operation is aborted, and the fioErrCode.FACE_DUPLICATION error code is raised to your host application instead. This option is immediately available to trigger from the FACEIO Console. Please refer to the updated boilerplate code to see this feature in action.

When this option is disabled (default case), it is considered, a no-error operation for the same user to enroll twice or more on your application. This will trigger a collision, and ask the user to confirm his PIN code whenever an authentication operation occurs with the user in question.

Session Reloading without Page Refreshing:

The introduced restartSession() method let you purge the current user session and request a new one. By default, sessions in FACEIO starts following the first call to the enroll() or authenticate() method whichever comes first, and are immutable. After successful call to this method, the old session is discarded, a new one is created, and you can perform another round of calls to enroll() or authenticate() for the same user without reloading (refreshing) the entire HTML page or relying on an external JavaScript function such as location.reload() to do so.

Please note that dynamic session generation via this method are available to the Premium Plans only. Think of upgrading your Freemium application from the FACEIO Console first before calling this method on your frontend

Finally, for additional information & to learn more about implementing FACEIO on your website or web application, please refer to the following documents:

• Getting Started Tutorial: Learn the fundamentals about implementing facial authentication on a typical web application.
• Integration Guide: Learn how to implement fio.js, our facial recognition library on your website.
• Developer Center: Code samples, documentation, support channels, and all the resources yo need to implement FACEIO on your website.
• Frequently Asked Questions: Get instant answers to the most common questions.
• Trust Center: Learn how we handle your data securely and in compliance with privacy and legal requirements.

We are pleased to share our Press Release launch document, as well as, new enhancement introduced to FaceIO, our Web based facial Recognition framework for Websites & Web applications with a set of new languages pack for a more than friendly user experience (UX).

Since its launch two months ago, FaceIO have been proven to be a rock solid product, with a slick & easy integration regardless of the underlying JavaScript framework whether it is React.js, Next.js, Vue.js, Angular or even Vanilla JavaScript. As of this announcement, Over 10K FaceIO Applications has been deployed, easing users authentication thanks to a passwordless experience powered by face recognition.

Available Language Pack

• English (en Default, fallback language)
• German (de)
• Portuguese (pt)
• Spanish (es)
• Dutch (nl)
• Japanese (ja)
• French (fr)
• Vietnamese (vi)

Upcoming Language Pack

• Korean (kr)
• Arabic (ar)
• Italian (it)
• Simplified Chinese (cz)
• Polish (pl)
• Russian (ru)
• Czech (cs)
• Indonesian (id)
• Malay (ms)
• Turkish (tr)

FaceIO Launch Press Release

• Biometric Update: PixLab launches face biometrics authentication framework for web applications.
• Symisc Systems Press Announcement: https://cdn.faceio.net/fio-press-release.pdf.

Community Articles & Tutorials

It’s super quick to implement FaceIO, and get it up & running on your website or web application. The following tutorials, and guides should help you roll facial recognition to your web users in a straightforward manner:

• Getting Started Tutorial: Learn the fundamentals about implementing facial authentication on a typical web application. Your first steps with FaceIO...
• Integration Guide: Learn how to implement fio.js, our facial recognition library on your website before rolling facial authentication to your audience...
• Developer Center: Code samples, documentation, support channels, and all the resources yo need to implement FaceIO on your website.
• Frequently Asked Questions: Get instant answers to the most common questions.
• Trust Center: Learn how we handle your data securely and in compliance with privacy and legal requirements.
• Implement Facial Authentication on your Vue.js App.
• How to Authenticate a User with Face Recognition in React.js.
• Face Log in/Sign up form using FaceIO, Next.js and Tailwind CSS.
• How to Authenticate a User via Face Recognition in Your Web Application.
• How to use Facial Recognition to Enhance user Experience on your Website using FaceIO.
• Using React-Typescript With FaceIO for User Face Authentication.

As requested by our users, the following are the list of endpoints that is provided by PixLab for all your face detection, recognition, generation & landmarks extraction tasks. These includes:

• /facedetect: State-of-the-art face detection.
• /facelandmarks: Used for extracting facial features such as the eyes, bone, mouth coordinates and thus to Mimic the famous Snapchat filters.
• /facecompare which is used for face to face comparison. In other words, it is used for recognition.
• /facelookup: find a person’s face in a set or group of people.

Here is two working Python code to illustrate this:

1.Detect all human faces present in a given image or video frame via facedetect and extract each one of them via crop:

import requests
import json
     
    # Target image: Feel free to change to whatever image holding as many human faces you want
    img = 'http://cf.broadsheet.ie/wp-content/uploads/2015/03/jeremy-clarkson_3090507b.jpg'
     
    req = requests.get('https://api.pixlab.io/facedetect',params={
        'img': img,
        'key':'My_Pix_Key',
    })
    reply = req.json()
    if reply['status'] != 200:
        print (reply['error'])
        exit();
     
    total = len(reply['faces']) # Total detected faces
    print(str(total)+" faces were detected")
     
    # Extract each face via crop now 
    for face in reply['faces']:
        req = requests.get('https://api.pixlab.io/crop',params={
            'img':img,
            'key':'My_Pix_Key',
            'width': face['width'],
            'height': face['height'],
            'x': face['left'],
            'y': face['top']
        })
        reply = req.json()
        if reply['status'] != 200:
            print (reply['error'])
        else:
            print ("Face #"+str(face['face_id'])+" location: "+ reply['link'])

2. Detect all human faces in a given image via facedetect and apply a blur filter to each one of them via mogrify:

import requests
import json

img = 'http://anewscafe.com/wp-content/uploads/2012/05/Brave-Faces-Group-shot.jpg' 

# Detect all human faces in a given image via facedetect and blur all of them via mogrify.
req = requests.get('https://api.pixlab.io/facedetect',params={
    'img': img,
    'key':'Pix_Key',
})
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
    exit();

total = len(reply['faces']) # Total detected faces
print(str(total)+" faces were detected")
if total < 1:
    # No faces were detected, exit immediately
    exit()
# Pass the detected faces coordinates untouched to mogrify 
coordinates = reply['faces']
# Call mogrify & blur the faces
req = requests.post('https://api.pixlab.io/mogrify',headers={'Content-Type':'application/json'},data=json.dumps({
    'img': img,
    'key':'PIXLAB_API_KEY',
    'cord': coordinates #The field of interest
}))
reply = req.json()
if reply['status'] != 200:
    print (reply['error'])
else:
    print ("Blurred faces URL: "+ reply['link'])

Further code samples are available on the PixLab Github repository or refer to the PixLab Endpoints list for the official documentation.