IntelligentFaceNet: Designing a multi-cascaded attentive and adaptive deep learning network for facial recognition using heuristic approach

Abstract

One of the significant application of robot vision technique that serves in biometric verification systems is Face Recognition (FR) technology. However, the effectiveness of the model is affected by disturbance from the real-world environment, including alterations in lighting, facial occlusion, and fluctuation in poses. Even though the recognition of faces has gained popularity due to the variety of applications, it still remains a complicated task to recognize because of an enormous variety of biometric data features. In the past few years, FR technology has seen a lot of development in this area of biometric and computer vision-oriented applications. The most important steps in creating an accurate FR system are the extraction of features and the categorization of these extracted features. The conventional method of feature extraction includes frequency domain features or the Eigenface technique. However, they are not robust to altering external factors like occlusion, illumination, and posture. Using deep learning techniques, a unique approach to address this problem in FR is suggested to recognize faces successfully. The images are aggregated in the initial phase. It is then uploaded to the Viola–Jones Face Detector tools to find the face. The detected face images are then subjected to the Adaptive Deep Convolutional Generative Adversarial Network (ADCGAN) to remove the occlusion from the facial images, where the parameters are subsequently optimized using the Modified Random Variable-based Galactic Swarm Optimizations (MRV-GSO) algorithm. The occlusion-removed face images are then given to the Multi-Cascaded Attentive and Adaptive Deep Learning Network (MCADN) model, where the outputs from the Dilated DenseNet and Residual Network (ResNet) are serially passed to the Bidirectional Long-Short Term Memory (Bi-LSTM) model forming the MCDAN. The MRV-GSO algorithm is performed to tune the hyperparameters in the MCADN model to produce the final recognized facial images. As a result of conducting several experimental studies, it is proved that the generated model outperformed standard approaches in terms of effective recognition rate.