A competitive-collaborative nonnegative representation method and its application for face recognition in smart campus

The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.

In recent years, significant achievements have been achieved in the field of face recognition. Face recognition are special pattern recognition which are used in banking for identity approving and the entrance of controlled areas, the places where the security control impending to airports, to control machines, to follow-up of persons. In this study, face recognition applications on the Yale face databases have been performed by using PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), LBP (Local Binary Patterns). Database consist of right-light, center-light, left-right, no glasses, glasses, normal, wink, sleepy, surprised, happy, sad images. In the face recognition pre-processing steps, HE (Histogram Equalization), HE+ Median Filter, HE+Gaussian filter, He+Laplace Filter was used. It is provided to select the appropriate method from the system depending on state of image. Thus the system recognition rate increases of up to 6% was observed. Application was developed by using Microsoft Visual Studio 2010 C #. EMGU CV library for image processing algorithms and SQL Server 2008 Express for database processing were used.

Noise modeling and representation based classification methods for face recognition

In the field of face recognition, the small sample size (SSS) problem and non-ideal situations of facial images are recognised as two of the most challenging issues. Recently, Zhu et al. proposed a patch-based collaborative representation (PCRC) method which showed good performance for the SSS and the single sample per person problems; and Peng et al. proposed a locality-constrained collaborative representation (LCCR) method which achieved high robustness for face recognition in non-ideal situations. Inspired by the methods proposed in PCRC and LCCR, this study proposes a patch-based locality-enhanced collaborative representation (PLECR) method to combine and enhance the advantages of both PCRC and LCCR. The PLECR and several related methods are implemented on AR, face recognition technology and extended Yale B databases; and the extensive numerical results show that PLECR is more efficient among these methods for the SSS problem in non-ideal situations, especially for the SSS problem with occlusions.

Representation-based classification (RC) methods, such as sparse RC, have shown great potential in face recognition (FR) in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression, or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression (MR)-based atomic representation and classification (MRARC) framework to alleviate such limitations. MR is a robust regression framework which aims to reveal the relationship between the input and response variables by regressing toward the conditional mode function. Atomic representation is a general atomic norm regularized linear representation framework which includes many popular representation methods, such as sparse representation, collaborative representation, and low-rank representation as special cases. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal FR, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust FR and reconstruction.

In recent years, local feature descriptors have received more and more attention due to their effectiveness in the field of face recognition. Local Derivative Patterns (LDPs) for local feature descriptions attract researchers' great interest. However, an LDP produces 2 p different patterns for p neighbors through the transition of LDPs for an image, which lead to high dimension features for image analysis. In this paper, LDPs are expanded to Uniform Local Derivative Patterns (ULDPs) that have the same binary encoding way as LDPs but different transition patterns by introducing uniform patterns. A uniform pattern is the one that contains at most two bitwise transitions from 0 to 1 or vice versa when the binary bit is circular. Then, the number of the transition patterns is reduced from 2 p to p(p?1)+3 for p neighbors, e.g., 256 to 59 for p?=?8. For face recognition, the histogram features are combined together in four directions, and both non-preprocessed and preprocessed images are used to evaluate the performance of the proposed ULDPs method. Extensive experimental results on three publicly available face databases show that the proposed ULDPs approach has better recognition performance than that obtained by using the LDPs method.

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

As the representative one of representation-based classification (RBC) methods, collaborative RBC (CRC) has drawn much attention in pattern recognition and machine learning recently. Moreover, the collaborative representation-based face recognition has been extensively studied because of the effective classification performance of CRC. CRC collaboratively represents each query sample as the linear combination of all the training samples and then classifies the query sample according to the categorical representation-based distances. However, most variants of CRC cannot fully consider the locality and discrimination of data and cannot well handle the noise data, which has negative effect on real-world classification problems, such as face recognition. In this paper, a new discriminative collaborative neighbor representation (DCNR) method for face recognition is proposed by integrating class discrimination and data locality. In the proposed method, the locality of data constrains collaborative representation of each query sample to find representative nearest samples of the query sample. Moreover, the class discrimination regularization is taken into account by employing the representation of each class for each query sample. Due to the existing noises, such as corruptions and occlusions in face recognition, we further propose robust DCNR (R-DCNR) for robust classification by using the $\ell _{1}$ -norm representation fidelity. Extensive experiments on face databases demonstrate that the proposed methods achieve competitive classification performance, compared to the state-of-the-art representation-based classification methods.

Extended interval type-II and kernel based sparse representation method for face recognition

A class-specific mean vector-based weighted competitive and collaborative representation method for classification

The varying quality of face images is an important challenge that limits the effectiveness of face recognition technology when applied in real-world applications. Existing face image databases do not consider the effect of distortions that commonly occur in real-world environments. This database (QLFW) represents an initial attempt to provide a set of labeled face images spanning the wide range of quality, from no perceived impairment to strong perceived impairment for face detection and face recognition applications. Types of impairment include JPEG2000 compression, JPEG compression, additive white noise, Gaussian blur and contrast change. Subjective experiments are conducted to assess the perceived visual quality of faces under different levels and types of distortions and also to assess the human recognition performance under the considered distortions. One goal of this work is to enable automated performance evaluation of face recognition technologies in the presence of different types and levels of visual distortions. This will consequently enable the development of face recognition systems that can operate reliably on real-world visual content in the presence of real-world visual distortions. Another goal is to enable the development and assessment of visual quality metrics for face images and for face detection and recognition applications.

One of the main areas in computer vision is automatic face recognition which deals with detecting human face autonomously. Developments and the progress in the field of face recognition have shown that many face recognition systems and applications the automated methods outperform humans. The conventional Scale-Invariant Feature Transform (SIFT) is used in face recognition where they provide high performances. However, this performance can be improved further by transforming the input into different domains before applying SIFT algorithm. Hence, we apply Discrete Wavelet Transform (DWT) or Gabor Wavelet Transform (GWT) at the input face images, which provides denser and extra information to be used by the conventional SIFT algorithm. Matching scores of SIFT from each subimage is fused before making final decision. Simulations show that the proposed approaches based on wavelet transforms using SIFT provides very high performance compared to the conventional algorithm.

Promoting the degree of campus informationization has become an important part of the construction of smart campus. Face recognition, with its uniqueness and lifelong invariance, has natural security advantages and practical advantages in the application of smart campus, but it also has the disadvantages of high equipment cost and data leakage risk. The face recognition technology can be used to build safe educational environment, create convenient business processes and develop personalized technology service in the construction of smart campus. This paper designs and implements a dormitory management system based on face recognition. The test results show that the use of face recognition technology as a source of information collection for people entering and leaving dormitory area greatly improves the management efficiency and ensures the accuracy and reliability. The application of face recognition in campus management system will break the traditional mode of teachers and students’ work and life, and become an important part of smart campus.

Over the years, face recognition has been the research topic that has attracted many researchers around the world. One of the most significant applications of face recognition is the access control system. The access control system allows authorized persons to enter or exit certain or restricted areas. As a result, it will increase the security situation without over-investment in staff security. The access information can be the identification, time, and location, etc. It can be used to carry out human resource management tasks such as attendance and inspection of employees in a more fair and transparent manner. Although face recognition has been widely used in access control systems because of its better accuracy and convenience without requiring too much user cooperation, the 2D-based face recognition systems also retain many limitations due to the variations in pose and illumination. By analyzing facial geometries, 3D facial recognition systems can theoretically overcome the disadvantages of prior 2D methods and improve robustness in different working conditions. In this paper, we propose the 3D facial recognition algorithm for use in an access control system. The proposed algorithm includes the preprocessing, feature extraction, and classification stages. The application of the proposed access control system is the automatic sliding door, the controller of the system, the web-based monitoring, control, and storage of data.

Face recognition technology has always been a hot research topic in the computer vision community, and has developed rapidly in recent years. Face recognition aims to build a model and predict the face identity information in a given image, which has been widely used in various aspects of social life, such as identity authentication, security encryption, human-computer interaction, etc. In order to improve the accuracy and speed of face recognition, and how to maintain good face recognition under the premise of occlusion, many advanced technologies have been proposed. This paper summarizes the face recognition technologies proposed in recent years, and introduces the latest research progress in the field of face recognition from two aspects: traditional face recognition based on manual features and face recognition based on deep learning. Specifically, we first briefly introduce traditional face recognition methods. Second, we introduce the mechanism of traditional Convolutional Neural Networks(Hereinafter referred to as CNN) in face recognition. Finally, we focus on the application of Transformer in the field of face recognition. According to the datasets used by the methods introduced above, the performance of these methods is summarized, the advantages and disadvantages of CNN and Transformer are pointed out, and the future development direction is proposed.