Abstract
Biometric applications have massive demand in today’s era. The areas of
applications are mostly linked with the security of the system. Biometric features are
regarded as the primary resource for security purposes due to their own distinctiveness
and non-volatile essence. System authentication using biometrics is considered to be a
sophisticated technology. Noise effect inducts variation in the biometric subject that
causes an adverse impact on establishing the recognition. The proposed model
supported the development of an effective method for performing facial biometric
feature recognition. The model's goal is to reduce the number of false approvals and
refusals. The proposed algorithm has been applied over a video dataset containing
surveillance video frames that capture facial subjects dynamically. The first step is the
pre-processing of the video frames that have been carried out in the proposed model.
Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video
frames. Feature extraction from the facial subject has been accomplished by applying a
deep reinforcement learning algorithm. Further, the proposed model applied a
convolutional neural network (CNN) algorithm to perform feature recognition of facial
identity accurately. The proposed technique aims to maintain a huge recognition rate of
dynamic facial subjects under various unprecedented noise variations. In the
classification algorithm, the recognition accuracy is found to be 98.85%