Facial Attendance System
Automate the process of taking attendance using Pixuate's Technolgy
IntroductionPixuate’s Face Recognition Attendance Systems are built using Deep Neural Networks and can help you in cost saving because these systems are available at affordable prices. These devices help in bringing punctuality in the organization because they keep records of employees in and out timings. The software for these systems is capable of generating industry standard reports. They contain a feature of admin lock in which only admin would be able to make any changes in the machine data. Pixuate’s Face Recognition Attendance Systems are best suited for factories, construction sites, labor attendance and even schools and colleges.
Key Features Of Facial Attendance System
- Uses the state of the art Deep Neural Networks (DNNs)
- Trained with 10 Million faces
- Can identify the frequency of occurrence of each face
- User friendly Pixuate GUI to capture & recognize
- Records the time, date & place of capture of each face image
- User can add any new face anytime
- Ability to identify faces with tilts
- Use of super resolution for low quality videos which increases the accuracy by 4X
- Works with multiple live cameras & offline videos with face images as small as 40X40
- Keeps records of daily, monthly and yearly attendance records.
How it Works ?
Pixuate Facial Recognition Attendance system analyzes the features of a person's face images input through a digital video camera. It measures the overall facial structure, including distances between eyes, nose, mouth, and jaw edges. These measurements are retained in a database and used as a comparison when a user stands before the camera. This type of biometric has been wild, touted as a fantastic system for recognizing potential threats i.e. whether terrorist, scam artist or known criminal. Every face has numerous, distinguishable landmarks, the different peaks, and valleys that make up the distinctive facial features.The process can be summarized with the steps below:
Capture & Detection:A physical or behavioral sample is captured by the system during enrollment. Pixuate systems can capture up to 4 different live feeds and detect faces.
Alignment: Once it detects a face, the system determines the head's position, size and pose.
Representation:The system then measures the facial features and creates a template. The system translates the template into a unique feature vector. This gives each template a set of numbers to represent the features on a subject's face.
Matching: The system then decides if the features extracted from the new sample are matching to the database or not.
Identification: Then the image is compared to all images in the database resulting in a score for each potential match (1: N). In this instance, you may take an image and compare it to a database of mug shots to identify who the subject is.
Update the Attendance Records:Once the face is recognized, the attendance records are updated with the name, time and date of the person’s entry and exit.