Mutual Component Convolutional Neural Networks for Heterogeneous Face Recognition


For example, the goal of heterogeneous face recognition (HFR) is to recognise people in photos that are both visible and near-infrared. There is a huge disparity in the number and type of training samples available for HFR. The MC-CNN, a modal-invariant deep learning framework, is proposed in this research to address both of these concerns simultaneously. As an FC layer, mutual component analysis (MCA) is incorporated into modern deep CNNs by our MC-CNN, which is a generative module. This FC layer is meant to extract modal-independent hidden components and is updated based on the maximum likelihood analytic formulation instead of back propagation, which naturally minimises overfitting from limited data.. The network for modal-invariant feature learning is also updated using an MCA loss. Our MC-CNN outperforms numerous fine-tuned baseline models significantly, according to extensive experiments. On the CASIA NIR-VIS 2.0, CUHK NIR-VIS, and IIIT-D Sketch datasets, our approaches outperform the state-of-the-art.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Siamese Content-Attentive Graph Convolutional Network For Personality Recognition Using Physiology ABSTRACT: Affective multimedia information has long been employed as a source of stimulation in the study
PROJECT TITLE : Convolutional Recurrent Neural Networks for Glucose Prediction ABSTRACT: Blood glucose control is critical for diabetes management. Machine learning techniques are used in current digital therapy approaches for
PROJECT TITLE : 3D APA-Net 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images ABSTRACT: Diagnostic and treatment of prostate illnesses, particularly cancer, rely heavily on accurate
PROJECT TITLE : Progressively Trained Convolutional Neural Networks for Deformable Image Registration ABSTRACT: The quick registration periods of deep learning-based algorithms for deformable picture registration make them viable
PROJECT TITLE : Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution ABSTRACT: The task of super-resolution of depth images is both significant and difficult. In order to deal with this

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry