My Account | Careers | Downloads | Blog

PROJECT TITLE :

Single sample face recognition - 2016

ABSTRACT:

Due to its wide applications in practice, face recognition has been an active research topic. With the availability of adequate training samples, many Machine Learning methods could yield high face recognition accuracy. However, under the circumstance of inadequate training samples, especially the extreme case of having only a single training sample, face recognition becomes challenging. How to deal with conflicting concerns of the small sample size and high dimensionality in one-sample face recognition is critical for its achievable recognition accuracy and feasibility in practice. Being different from the conventional methods for global face recognition based on generalization ability promotion and local face recognition depending on image segmentation, a single-sample face recognition algorithm based on locality preserving projection (LPP) feature transfer is proposed here. First, transfer sources are screened to obtain the selective sample source using the whitened cosine similarity metric. Second, we project the vectors of source faces and target faces into feature subspace by LPP, respectively, and calculate the feature transfer matrix to approximate the mapping relationship on source faces and target faces in subspace. Then, the feature transfer matrix is used on training samples to transfer the original macro characteristics to target macro characteristics. Finally, the nearest neighbor classifier is used for face recognition. Our results based on popular databases FERET, ORL, and Yale demonstrate the superiority of the proposed LPP feature transfer-based one-sample face recognition algorithm when compared with popular single-sample face recognition algorithms, such as (PC)2A and Block FLDA.


Did you like this research project?

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

Matrix Algebra Feature Extraction Image Segmentation Face Recognition Learning (Artificial Intelligence) Transfer Learning One-Sample Locality Preserving Projection
PROJECT TITLE : Robust Rank-Constrained Sparse Learning: A Graph-Based Framework for Single View and Multiview Clustering ABSTRACT: Graph-based clustering is an approach that seeks to partition data in accordance with a similarity
PROJECT TITLE : Detailed Avatar Recovery from Single Image ABSTRACT: In this paper, a novel framework for recovering detailed avatar information from a single image is presented. Variations in human shapes, body poses, textures,
PROJECT TITLE : Accurate Transmission Estimation for Removing Haze and Noise From a Single Image ABSTRACT: Image noise frequently results in depth-dependent artefacts while dehazing a single image. For noisy and foggy inputs,
PROJECT TITLE : Confidence Measure Guided Single Image De-Raining ABSTRACT: Single-image de-raining is a difficult subject since rain streaks fluctuate in size, direction, and density in wet photos. Different sections of the image
PROJECT TITLE : Fast Single Image Dehazing Using Saturation Based Transmission Map Estimation ABSTRACT: Dehazing a single photograph has proven to be a difficult task due to the image's unnatural lighting. As a result, several

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

Project Enquiry