Everything About

Face Reader Software

What is Face Reader software?

Face Reading Software uses AI algorithms to detect faces and facial features, analyze facial features to determine expression/emotion, and identify demographic data such as age and gender. These AI algorithms are trained on large datasets and create templates for the most common expressions, including: neutral, happy, sad, angry, surprised, fearful, disgusted, and sometimes also contemptuous.

Different software programs use different methods and different types of AI algorithms to carry out the various steps involved in the face reading process. Deep learning methods of AI, for example, can be used to increase the accuracy of face detection, which is the first step in face reading.

How to use Face Reader software

Face Reader Software requires input in the form of digital images or videos. For this purpose, the software can be directly connected with a camera for real-time analysis, or it can be fed image/video uploads manually.

Face Reader Software can be used for real-time face reading in the following types of situations:

  • When you want to identify the demographics and gauge the emotional responses of the people who are viewing your digital signage campaigns in order to tailor the content to your audience in real time
  • When you want to conduct market research or user experience studies by observing people as they browse through a website or interact with a product
  • When you want to use the software for human-computer interaction, such as controlling a wheelchair using facial expressions or using driver-assist technology in a vehicle to make sure that the driver stays awake.

Face Reader technology can also be integrated with hardware or other software programs to create broader solutions.

How to choose the right Face Reader software

Before you choose which Face Reader Software to implement, you need to consider the following:

  • What is your most important reason for using the software? Is it recognizing emotion, determining demographics, or something else? Once you have the answer, you can focus on finding a software solution that performs well in your main focus area.
  • Do you want a ready solution or do you want to build something yourself? If you’re looking for a ready-to-use solution, you can consider a product such as Sightcorp’s DeepSight Toolkit. If you want to integrate Face Reader Software into an existing product, or if you want to build something yourself, then it would be best to consider an API or SDK.
  • What platform do you want to use the software on? Whether you want to use the software on mobile (iOS, Android, etc.), desktop (Windows, MacOS, Linux, etc.), or elsewhere, you need to make sure that your chosen solution is compatible.
  • Do you want to run the software online or offline? Analyzing video over network or in the Cloud is usually very complex and can become quite expensive. Therefore, if you don’t require such analysis, your best choice is to process everything offline. This is also a safer choice in terms of data protection and privacy. You can always share the gathered data with third-party platforms (e.g. dashboard, content management systems) hosted in the Cloud after the processing is done. If, on the other hand, you want to conduct market research or user testing remotely, online may be your best option. Consider your needs and the available options before you make a decision.
  • Do you want privacy by default? If you are operating within the European Union, then this is definitely something you need to consider. In the context of Face Reading Software, privacy by default could mean that faces in videos are blurred automatically, unless you disable this option yourself.
  • Do you want to analyze images, video, or both? Make sure you choose software that allows you to analyze faces in the format that you want it to.

Other uses of Face Reader software

Face Reader Software can be integrated into Facial Recognition technology to assist with liveness detection. For example, a user may be required to smile or to wink to prove that there is a real person behind the camera, not a photo or mask to fool the facial recognition software.