Before we talk about the best open source face recognition projects, let’s briefly discuss face recognition technology and its applications.
What is face recognition technology?
Face identification is a biometric technology that can distinguish people from each other and verify individuals’ identity by analyzing the patterns it obtains from a person’s face.
Face recognition technology has recently been used in various fields, from unlocking smartphones to critical security applications in different organizations. With the ability to be used for multiple purposes and in different areas, face recognition technology has become a popular application among various organizations and companies.
How does face recognition technology work?
First of all, the system is trained what a face really is to distinguish it from other things around. To do this, programmers usually introduce a number of images to a specific algorithm.
After several trials and errors, the algorithm can analyze new photos well and find the faces’ approximate location. This is the first step to face recognition.
In the next step, the programmer train the system on how to distinguish one face from another. There are several ways to do this. For example, some algorithms map the faces and measure the pupillary distance (PD), the distance between eyebrow and eye, nose and mouth, and generally the distances between all face components.
To identify a particular face in crowded places such as a sports stadium, face recognition technology defines all individuals’ faces to create a separate vector for each. The software compares these vectors with the person sought’s face pattern and finds the most similar ones in the next step. However, if the wanted person’s facial information is not available, the search will be ineffective.
Here we briefly introduce the six best face recognition projects. The source of these projects is available to all programmers. If you need face recognition software, you can choose the most suitable one.
The best open source face recognition projects:
- OpenBR
An open-source biometric framework supports the development of open algorithms and repeatable evaluations. The stable version of this program (version 11) was released on September 29, 2019. This software works on Windows, OS X, Linux, and Rasbian. OpenBR uses the 4SF2 algorithm to detect faces and can also identify age and gender.
- Flandmark
Flandmark is an open-source C library. This software performs face recognition in still (not moving) images. Windows, Linux, and Mac support this software. The first version was released in August 2011. One version of Flandmark (version 1.06) can be used in Python, but the software was initially written in C, C ++, and MATLAB.
- OpenFace Tracker
This software is one of the leading facial recognition software that can recognize the face in photos and videos. OpenFace Tracker can be used on Windows. It uses real-time analysis via Google, and its performance speed is high. Running data offline prevents reloading and wasting time.
- OpenEBTS
Open source face recognition projects aim to develop biometric-related software, and OpenEBTS is one of these projects from ImageWare Systems. In addition to Windows, this software can be run on Android and Linux operating systems. Training related to this software is available on its website, and customers can use the program support by buying online tickets.
- Bioenable Tech iFace
iFace is a leading application in biometric technology. In addition to face recognition, iFace uses fingerprint sensors and a time attendance system. This software also performs peer-to-peer recognition and face grouping and is suitable for small and medium businesses. Bioenable Tech iFace is web-based.
- FaceFirst
FaceFirst is one of the most powerful open source face recognition software that can be used by organizations and offices. This software uses automated video analysis and helps companies and retailers to prevent theft, misuse, and forgery.
Limits of face recognition technology:
While face recognition programs can use different criteria to identify faces, they also have limitations. Some of these limitations are as follow:
- Poor resolution images and low environmental light can reduce the accuracy of facial scan results.
- Different angles and facial expressions -even a simple smile- can pose challenges for facial recognition systems.
- If a person uses items such as glasses, hats, scarves, or change her/his hairstyles or covers a part of the face, biometric face recognition may experience a real challenge. Heavy makeup and bearding can also make it difficult for facial recognition programs to identify.
- Face scanning does not necessarily link to a profile, which means that scanning a person’s face is useless if there is no image of the person’s face in the database. Without matching, the identity of the person whose face is scanned remains a mystery.
Depending on the database’s size, computers may need a lot of time processing the faces. Sometimes – for example, in security cases – this restriction can be problematic.
