What is face recognition attendance system?
Face recognition attendance system uses a person features to identity a person and mark its attendance. Facial data of the person is stored while registration of the face and that information is used to verify a person and automatically mark an attendance.
The face recognition attendance system uses the contact less and most hygienic way to mark the attendance. Now a days face detection attendance system are start to capture the attendance from a distance of up to 3 meters. Face recognition for attendance monitoring system is used by lots of organization for automatic attendance capturing. Not just for the attendance capturing face detection attendance system comes with payroll for salary calculation. In times of the COVID-19 pandemic face detection attendance with temperature is used to identify and store the temperature of the employees coming in the organization.
A touch less system is an effective way to capture the attendance of the employees. Face recognition based attendance app doesn’t require any special hardware. It can work on on any Android or iOS devices. It’s a cloud-based touch less attendance system. With the features like Geo Fencing it can be used for Office attendance, Contract staff attendance. Geo Location tracking can be used to capture the attendance of the Field employees. Liveliness Check (Fake Recognition) is done by checking the movement of the person.
How face detection attendance system works?
Face detection attendance system uses following components.
- A face detector
- A feature extractor
- A matcher
A face detector detects the liveliness of the person.
A feature extractor extracts the features of the face which is further feed to the matcher.
A similarity score is used with the query face and each enrolled face which is generated by the matcher and used for verification.
Over the past four decades, there has been significant progress in automatic face recognition for Attendance System.
Many face attendance system allow to set the threshold for the Verification. This is set by computing the similarity scores of all samples pairs in the database. The threshold helps in designing the best performance based on one of the following metrics.
- False acceptance rate (FAR)
- False rejection rate (FRR)
- Equal error rate (EER)
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