Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding

Abstract

In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, this paper presents an effective method that exploits nonlocal sparsity by estimating the sparse code changes, which can be done by adding a nonlocal constraint term to the local constraint one. In addition, for generality, the sparse coding and regularization parameters are adaptively estimated. A comparative study demonstrated that the proposed method has better accuracy rates compared to the existing state-of-the-art methods.

New Face Recognition System Based on DCT Pyramid and Backpropagation Neural Network

“…In this section, we consider the three well-known data sets, namely the Olivetti Research Laboratory data set (ORL), Yale, and AR to evaluate the performance of the proposed face recognition system that is based on the DCT pyramid coupled with ANN. In the following subsections, various experimental results are thoroughly analysed and compared in terms of accuracy and effectiveness with relevant and recent published methods [5], [19], [29], [30], [34]- [41].…”

“…This standardised format ensures consistency and simplifies subsequent computations and feature extraction processes. The ORL data set has proven to be a valuable resource for research and development in facial recognition and related fields, due to its diverse and well-organised nature [5], [19], [29], [30], [34], [35], [37], [39], [40]. Several tests are conducted to assess the performance of the proposed system by varying decomposition ratios for training and testing the data set.…”

“…Table I provides an overview of the perfect performance achieved by the proposed system in the ORL data set. To show the remarkable performance of the proposed system on the ORL data set, comprehensive comparisons are conducted with other state-of-the-art methods, specifically those reported in [5], [29], [30], [34], [35], [37], [39], [40]. Table II presents a detailed breakdown of the recognition rates attained by each of the mentioned methods with five images used for training in the proposed method and clearly shows that the proposed system achieves an impressive recognition rate of 100 %.…”

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

“…Nowadays, machine learning (ML) techniques have been widely explored and applied in almost all Internet companies, and serving as essential parts in diversified fields, such as recommendation system [1]- [3], fraud detection [4]- [6], advertising [7]- [9], and face recognition [10], [11], etc. With the help of these techniques, excellent performance and significant improvement have been obtained.…”

High Order Volumetric Directional Pattern for Video‐Based Face Recognition

“…However, there are some illumination limitations since this method deals with the small local neighborhood of a pixel as well as utilizing the image intensity directly. While there are other methods for still and video-based face recognition procedure that do not rely on the image features, they depend on the sparse representation based categorization strategy, which considers the local sparsity identification from sparse coding coefficients [7,8]. Zheng et al [9] recently introduced a full system for unconstrained video-based face recognition, which is composed of face/fiducial detection, face association, and face recognition.…”