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A non-intrusive technique to capture physical traits without contact and cooperation from people discovers its application in the acknowledgment framework. Every face can be illustrated as a linear combination of singular vectors of sets of faces. Thus, Principal Component Analysis (PCA) can be used for its implementation. The Eigen Face Approach in PCA can be utilized as it limits the dimensionality of a data set, consequently upgrading computational productivity [6].

WORKING OF FACIAL RECOGNITION TECHNIQUE:

Facial recognition technology identifies up to 80 factors on a human face to identify unique features. These factors are endpoints that can measure variables of a person’s face, such as the length or width of the nose, the distance between the eyes, the depth of the eye sockets and the shape and size of the mouth. In order to measure such detailed factors, complexities such as aging faces arise. To solve this, computers have learned to look closely at the features that remain relatively unchanged no matter how old we get. The framework works by capturing information for nodal points on a computerized picture of a person’s face and storing the subsequent information as a face print [7]. Face print is like a fingerprint but for your face. It accurately identifies the minute differences even in identical twins. It creates 3D models of your face and analyses data from different angles, overcoming many complexities associated with facial recognition technology. The face print is then utilized as a reason for correlation with information captured from faces in a picture or video.