Face recognition SDK provides fast and accurate results for identification, identity verification, access control, and personalization.
Our face recognition software is based on face descriptors – unique arrays of numbers that describe faces.
In real time, it calculates the similarity between the face descriptor of the newly detected face and all the previously enrolled descriptors.
You get quick and accurate results with any standard camera, including devices with resource constraints.
FaceRecognition doesn’t store, process or transmit personal information such as images or names, giving you full control over data.
All biometric templates are exclusively mathematical representations of users’ faces, strictly separating biometric and personal information. They cannot be exported or reverse-engineered to their original format.
This way of handling data ensures the highest level of privacy, even when dealing with extremely sensitive data.
Protect your data and premises, better understand your target audience, improve user experiences, and more. All you need is one SDK.
Use face recognition to improve:
Besides improving security, face recognition also facilitates personalization and provides valuable insights for your business.
You can combine it with complementary computer vision technologies – face tracking, eye gaze tracking, emotion recognition, age and gender estimation, and more – to acquire relevant data that can help you create better products and services.Explore visage|SDK
Build a face recognition system that meets your every need with cutting-edge face recognition technology.
Integrate our face recognition yourself, or rely on our experienced Research & Development team to build a custom turnkey solution for you. Take advantage of our:
Get in touch – our experts will be happy to answer your questions and activate your free trial.
Face recognition is an automated process for comparing faces. It relies on cutting-edge computer vision algorithms that detect human faces in images, videos, or live feeds and match them against a database of faces.
Face recognition helps automate tasks and processes, and provide better, more personalized user experiences.
It’s used across various industries, such as security, automotive , healthcare, retail, and more. The most common use cases include device and access control, attendance tracking, ID matching, driver recognition, etc.
Besides security, face recognition also helps facilitate personalization. Being able to recognize individuals in real time helps tailor products, services, and environments to their needs and preferences.
You can easily integrate FaceRecognition into your existing solution or rely on our in-house research and development team to create a custom solution or specialized features for you.
Fast. We typically match faces in a fraction of a second. This speed can be affected by image size, the number of faces in the image, and your specific use case.
Recently, visage|SDK was included in the independent NIST FR Vendor test (FRVT). Our FaceRecognition was the fastest and most accurate lightweight algorithm compared with all other submissions in the report. It stood out with an execution time of only 26.25 ms and memory usage of just 73 MB. Among all the lightweight submissions, it achieved the highest accuracy on unconstrained images with a False Positive Rate of around 0,0005%. These are images taken ‘in the wild’, without controlled conditions.
Read the full report here.
FaceRecognition is available for a wide range of platforms including desktop, mobile, web, and embedded devices. It can process a variety of file formats, including photos, videos, and live streams. Because of its extremely lightweight nature, it’s the perfect fit for edge devices.
FaceRecognition runs entirely on the client-side. There are no cloud API calls, no data transfers, and no need for a network connection to the device. Instead of sending data – such as facial images – to a server through an internet connection, FaceRecognition compares facial features from an image to the face descriptors already saved in an offline database.
That means that all the processed data stays on-device, without any intermediaries or third parties having access to it.
Liveness detection helps prevent spoofing attempts designed to deceive a face recognition system using a substitute for another person’s face (usually their photo, video recording or a 3D mask). If the spoofing attack succeeds, the fraudster acquires the privileges or access rights of another person.
Liveness detection prompts the user to perform a simple and quick facial action, such as a smile or a blink, then verifies whether the action was performed. This ensures that there’s truly a live person in front of the camera.