Facial Recognition in Video Analysis
Facial recognition systems that can identify or verify a person from a digital image or video find application in a variety of contexts. Tag suggestions on Facebook, automated criminal identification from image/video footage, and access control integrated with facial biometrics are all facial recognition software in use.
Facial recognition works in two parts: face detection and face identification. In the first stage, the system detects faces in the input data using methods like background subtraction. Next, it measures the facial features to define facial landmarks and tries to match them with a known dataset. Based on the percentage of accuracy of match, the faces can be recognized or classified as unknown.
For instance, we used Dlib’s face landmark predictor to detect a face and extract features such as eyes, mouth, brows, nose, and jawline. The image was standardized by cropping to include just these features and aligning it based on the location of eyes and the bottom lip. The preprocessed image was then mapped to a numerical vector representation. An algorithmic comparison of the vector images made facial recognition possible.
Facial Recognition Systems at Workplace
Automated Attendance Tracking
The employee stands in front of the camera for a few seconds allowing it to capture his/her image. An integrated facial recognition system verifies the image with its training dataset and marks attendance on successful match.
Tracking Assets in Device Lab
The system detects the absence of a device from the shelf using background subtraction of CCTV images. With facial recognition capabilities, it will identify the person who entered the room during the time frame and assign the device to that employee.