A New Design of Occlusion Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Deep learning networks are considered as an important technique for face recognition and image recognition. Convolutional Neural Networks (CNN) is regarded as a problem solver in face recognition challenges. To solve the challenges of occlusion and noise in the image, more clarification is needed to acquire high accuracy. Hence, a deep learning model is developed in this paper. The proposed model covers four main steps: (a) Data acquisition, (b) Pre-processing, (c) pattern extraction, and (d) classification. The benchmark datasets with occluded faces is gathered from public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. By inputting the pattern extracted image, a deep learning model “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process. The experimental results are obtained and the proposed model gives better classification accuracy.