In today’s ever-evolving financial and business landscape, facial recognition is increasingly becoming a vital feature. It’s a digital solution that identifies, verifies and authenticates people. The technology achieves this by analysing and matching their facial features with faces stored in a known database.
It stretches beyond face detection, which only detects the presence of faces in videos, images or real-time. A face authentication solution features several steps and processes, including:
- Face detection: This step is crucial as it locates and extracts important regions of the face.
- Feature extraction: This is where facial features are extracted, including texture, shape, and position.
- Database comparison: Extracted facial features are matched to a pre-existing database of labelled or known faces.
- Matching and identification: The algorithm matches extracted features with the database to find exact matches or the closest match.
These are the steps that your face authentication solution goes through once you create it via an API. This article deep dives into the world of facial verification services via APIs to enable you to detect and recognise faces in real-time video streams and store videos or images right on your device. Let’s help you provide face verification services through an API.
Import the Packages Required for Your API
You must import the packages required for your API. An npm package manager can allow you to install these packages. Depending on your API, you can use either express, express-file-upload, mongoose, canvas or face-api.js. Once you install the packages, import them into the server file. After that:
- Initiate the express server
- Connect it to the database
- Listen to the desired port
Initiate Models
Your server must be working perfectly. If that is the case, load the model. If you’re using pre-trained models of, let’s say, face.api.js, download their saved models and initiate the face-api using these models.
Create a Database
To store your data, you must have a database first. You also need to define a schema. It’s vital to take note of the data types of your schema. Store your descriptions as array and labels as string.
Create-Face Route and Save the Data
You’ve got a schema and already loaded the model. That means you can start receiving labelled face photos and store them in your database. Define a function that receives a label and a set of images. Extract the face descriptions and store them in the database.
Fetch-face Route and Recognise Faces
You need to be very careful here. All the face data you collect from the database includes objects in arrays. For your model to interpret each image’s descriptions, it must be labelled-face-descriptions objects. To achieve that, you must pass in those descriptions as float32array. All of your objects need to convert into array and then to float-32array.
After that, a facematcher is initiated and the model can interpret the photo that has passed. You need to run the detection and processing functions depending on the face API’s documentation and return the outcomes.
Your API is ready to go! Test it using a great tool for testing API free of charge. Upload a test image and see if the API can correctly recognise whose face it is. If this process seems overwhelming, you can try a face verification service available via API. You’ll be able to take your face verification experience to a higher level, including:
- Digital onboarding
- Payments
- Online banking
- Government e-services
- Shops self-checkout
- Media sharing and social networks
All key features are also available, from simple and comprehensive API and security and privacy to presentation attack detection.