Face recognition camera software programs can identify and interpret complex patterns in ways similar to the human brain. The software uses biometrics to map the geometry of the face. It notes more than sixty facial landmarks, measuring and then calculating a number to represent the combined relative distances. The result is called a “faceprint”. When comparing the image input to a database of stored mathematical models of known faces, an algorithm will determine the best match and a percentage of probability of accuracy is provided. Additional related information, like skin tone, color and style of clothing, backpack detection, vehicle type, and travel direction, are also able to be determined by advanced programs.

In optimal conditions, facial recognition software can reach accuracy rates of over 99%. Accuracy can, however, deteriorate because of poor lighting, diverse faces, background noise and other factors. The software can also struggle with things like glasses, hats and hooded sweatshirts.

For years, face recognition has enabled the capture of wanted criminals. Face analysis is also a crucial support for theft prevention and safety in transportation and in retail. As facial recognition increases in efficacy, so does its number of commercial applications. There is particular growth in healthcare, IT, banking and marketing. Face scanners are increasingly used to improve customer experience and increase sales, by identifying celebrities and favored customers. Thanks to deep learning-based algorithms, face analysis is able to provide identity verification with a high degree of accuracy, even outperforming human facial recognition capabilities.

A reliable facial recognition software needs accurate algorithms and many diverse facial features to learn from. It uses machine learning to continually improve over time and adapt to different situations. Another key feature to keep in mind is privacy and making sure that measures are available to protect this. Lastly, the software should also be easy to use to avoid human errors when using it.

Facial recognition software relies on being able to measure facial features such as the eyes, mouth and nose. When distinguishing between faces, the structure and geometry of the face is the foundation, such as the distance from forehead to chin and the distance between the eyes. Additionally, it will also look at the balance of both sides of the face, thus determining the facial symmetry. By taking all these aspects into account, the software is able to create a facial profile which will then be compared to known profiles in the database.