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How To Build A Facial Recognition System?

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How To Build A Facial Recognition System?
How To Facial Recognition Software Work?

In 2021, with the wide usage of smartphones and face IDs facial recognition software is becoming more and more important every day. According to a study done by Progressive Markets, “The global image recognition market was valued at $13.7 billion in 2015, and is to reach $79.8 billion by 2025, growing at a CAGR of 19.7% from 2017 to 2025.”

It is clear that for those companies that can create innovative facial recognition solutions, there are enormous profits to be made. Therefore, it is important to understand a basic construct of how a facial recognition system is made. A few basics before we start! Facial recognition software belongs to the category of biometric software. This is software that maps people’s facial features. Such software uses mathematical mapping techniques and stores this data as “faceprints”. Subsequently, facial recognition software uses deep learning capabilities to compare a digital image with the face prints that were stored earlier. This way, such software can be used to allow identity verification. Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers.

These neural networks attempt to simulate the behavior of the human brain albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. Now that we have defined what exactly facial recognition software is and how it generally works, let's find out the steps it needs to follow. Firstly, the software captures an image of your face and stores it in its database. This process is usually stored on a raspberry pi camera coded in Golang using an Ubuntu OS. Then the software needs to recognize certain key geometric details from your face such as the distance between your eyes, the area covered by your cheeks, the width of your forehead, and so on. More generally, your face is divided into a slew of triangles, the properties of which are calculated and defined by the software.

This process can be done using two techniques, the first is the TensorFlow object detection model and the other is known as Caffe face tracking. Both of these methods are coded within the OpenCV library. Moreover, the software also recognizes certain key facial landmarks such as moles, mustaches, beards, and others. When recognizing a singular face, the software will browse its database of numerous facial signatures using deep learning algorithms and see if it finds a match. Once a face has been captured, the cropped image will be relayed with an HTTP form-data request to the back end. This facial image is then saved by the API, both on the local file system and in the detection log, appended with a personID.

On the back end, an algorithm uses the dlib function to generate a 128-dimension vector that details this face’s attributes and then cross-references this vector with all of the facial entries in the database using Euclidean distance in order to discover whether this new face is a match with any faces on record. If the face matches with the record, then the application gives access to whatever information is locked behind the software, if not a notification is generated and sent to whoever is the manager or supervisor of the information, and access is denied.

Now that we know what facial recognition software is and how it works we can gauge what is desired in a good facial recognition software. Summarily, a good facial recognition software requires a robust database, powerful matching algorithms, scalability, privacy protection, regular purging of surveillance data, and a good analytic recognition system. When particular facial recognition software can deliver on functionality and these features we can judge whether the instance is a success or a failure!