Joint Transform Correlation Technique for Face Recognition Essay
The joint transform correlator JTC is a correlation-based technique that consists of comparing two images to detect, localize or identify the reference in a target image. In this paper, we propose an optoelectronic two-layer neural network based on the Edge-adapted joint transform correlator for invariant face recognition, suitable for both in-plane and out-of-plane applications. In recent decades, interest in facial recognition theories and algorithms has grown rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous. A novel reference phase-encoded joint transform correlation technique is proposed for efficient multi-target detection. The proposed method uses phase encoding for the reference image and. The correlation result presents the zeroth order term, and two antiparallel correlation lines, as and, with the same information. AJTC's amplitude joint transform correlators are known to be robust against embedding and additive noise, and thus are a good solution for pattern recognition in these conditions. This feature makes Subspace learning approaches not widely used for facial recognition. Correlation approaches such as Vander Lugt Correlator VLC and Joint Transform Correlator JTC, 3. For facial recognition applications, it is necessary to have a robust discrimination system. This paper describes a new method for denoising the correlation plane and removing the zeroth term associated with JTC architectures for alternative joint transform correlators, such as the nonlinear JTC NJTC and nonlinear nonzero order JTC. The performance of the joint Fourier transform correlation JFTC algorithm has been improved by improving the horizontal edges of the frequency domain for the case where the reference and input scenes are located.