Since its inception in 1967, facial recognition technology has come a long way. Today it is more reliable and accurate with zero time lag. And for this very reason, today, it is used for a wide range of applications like Airport security, home security, cashless payment, identity verification, KYC, E-commerce, etc.
Facial recognition technology has a lot of potential, especially in the post-Covid world, where businesses actively seek to create a contactless experience for their customers and stakeholders.
One of the most promising applications of facial recognition is in the area of attendance management. Facial recognition attendance systems can automatically capture attendance data, eliminating the need for manual attendance sheets or time clocks. This can save businesses time and money, and it can also improve accuracy and security.
Recognizing this market opportunity, our team of experts built a face-recognition attendance system that tracks employee movement in real time.
The Purpose of the Face-Recognition Attendance System.
The team wished to build a secure and accurate attendance system that allowed employees contactless login to work. A system that helps organizations increase attendance tracking accuracy while improving security by barding entry of unrecognized or unauthorized individuals.
Prerequisites to building a face-recognition attendance system
You need the following tech stacks to create a face recognition attendance system.
Angular: Used version 15 with Typescript Version 5.0.4
NodeJs: Used version 16 with javascript version ECMAScript 2022
How Does the System Work?
Before diving into the implementation, let me give an overview of the face recognition process. Face recognition involves two processes: face registration and face matching.
- Face registration is a process of storing the features of a face in the file system or database.
- Face matching is a process to match the face between the detected features with the other existing features in our dataset, either using a classifier or distance metric algorithm.
Steps
- To perform face recognition, firstly, we have to perform a detection algorithm to find the location of faces.
- Then, the detected faces must be aligned properly before feature extraction.
- Next is to crop the region of interest (ROI) or face region from the original image.
- Then, feature extraction is applied to generate the feature data or feature vector representation.
- Finally, a matching algorithm such as Euclidean Distance or any classifier is applied to find the shortest difference in similarity between detected features and the features in our datasets.
Result
Using the latest tech stack, the team has built an efficient face recognition attendance system with fast image processing which is easy to integrate into the existing infrastructure. It improves employee attendance tracking while cutting costs.
Here is how our face recognition app can help improve attendance tracking
- Maintaining a manual sheet is unnecessary as the new system can accurately capture real-time employee movement.
- It improves security by preventing unauthorized individuals from accessing facilities and data.
- It creates a safe and contactless experience for employees and saves their efforts in manually clocking in or out.
- Facial recognition systems can detect fraud, such as employees clocking in for work when absent.
If your company is still using a manual or fingerprint-based biometric attendance system. Consider upgrading to a facial recognition attendance system for enhanced reliability, accuracy, and security in tracking employee attendance.
