Create and implement a new method for robust video face recognition using convolutional neural network algorithm

• Published in: e-Prime Vol. 5; p. 100241
• Main Authors: Kumar, Ankit, Yadav, Rakesh Kumar, Saini, DilipKumar Jang Bahadur
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023; Elsevier
• Subjects: Convolution neural network; Deep reinforcement; Face recognition; Pre-processing learning; Pre-processing learning; Face recognition; Convolution neural network; Deep reinforcement
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2023.100241

•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 captures facial subjects dynamically.•The first step is 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.•The proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately in the classification algorithm, the recognition accuracy is found to be 98.85%. 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 captures facial subjects dynamically. The first step is 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%.