Description

Face recognition is the process of identifying one or more people in images or videos by analyzing and comparing patterns. Algorithms for face recognition typically extract facial features and compare them to a database to find the best match. Face recognition is an important part of many biometric, security, and surveillance systems, as well as image and video indexing systems. Improvements in discriminative power come at a computational cost and with a risk of over-fitting. The system proposes a new approach to dense feature extraction for face recognition. This approach is Learning Compact Feature Descriptor and Adaptive Matching Framework. This approach consists of two steps. First, an encoding scheme is devised that compresses high-dimensional dense features into a compact representation by maximizing the intrauser correlation. Second, we develop an adaptive feature matching algorithm for effective classification. This scheme effectively converts high-dimensional dense features into a much more compact representation. Furthermore, we propose a new face matching method, called the ‘Adaptive Matching Framework’, and conduct experiments in four different face recognition scenarios: face recognition in the wild, aging face recognition, and matching near-infrared face images and optical face images, and the FERET test.