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Sparse representation scheme with enhanced medium pixel intensity for face recognition

CAAI Transactions on Intelligence Technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test sample.It has been widely used in various image classification tasks.Sparseness in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for classification.For deformable images such as human faces,pixels at the same location of different images of the same subject usually have different intensities.Therefore,extracting features and correctly classifying such deformable objects is very hard.Moreover,the lighting,attitude and occlusion cause more difficulty.Considering the problems and challenges listed above,a novel image representation and classification algorithm is proposed.First,the authors’algorithm generates virtual samples by a non-linear variation method.This method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable objects.The combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the algorithm.Thereby,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion scheme.The weighting coefficients in the score fusion scheme are set entirely automatically.Finally,the algorithm classifies the samples based on the final scores.The experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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Local-Tetra-Patterns for face recognition Encoded on Spatial Pyramid Matching

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Computers, Materials & Continua 2022年第3期70卷 5039-5058页

作者： Khuram Nawaz Khayam Zahid Mehmood Hassan Nazeer Chaudhry Muhammad Usman Ashraf Usman Tariq Mohammed Nawaf Altouri Khalid Alsubhi Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy Department of Computer Science University of Management and TechnologySialkotPakistan College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Computer Science Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah21589Saudi Arabia

face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and expressions.Providing a single solution to solve all these problems at a time is a challenging task.We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching.The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching andmax-pooling.Finally,the input image is recognized using a robust kernel representation method using extracted features.The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets.Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

关键词： face recognition local tetra patterns spatial pyramid matching robust kernel representation max-pooling

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Research on will-dimension SIFT algorithms for multi-attitude face recognition

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High Technology Letters 2022年第3期28卷 280-287页

作者：圣文顺 SUN Yanwen XU Liujing Pujiang Institute Nanjing Tech UniversityNanjing 211200P.R.China School of Information Engineering Nanjing Audit UniversityNanjing 211815P.R.China

The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.

关键词： face recognition scale invariant feature transformation(SIFT) dimensionality reduction principal component analysis-scale invariant feature transformation(PCA-SIFT)

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face recognition based on subset selection via metric learning on manifold

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Frontiers of Information Technology & Electronic Engineering 2015年第12期16卷 1046-1058页

作者： Hong SHAO Shuang CHEN Jie-yi ZHAO Wen-cheng CUI Tian-shu YU School of Information Science and Engineering Shenyang University of Technology The University of Texas Health Science Center at Houston Schulich School of Engineering University of Calgary

With the development of face recognition using sparse representation based classification(SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l1-minimization. Under this observation,several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.

关键词： face recognition Sparse representation Manifold structure Metric learning Subset selection

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face recognition Using Dense SIFT Feature Alignment

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Chinese Journal of Electronics 2016年第6期25卷 1034-1039页

作者： ZHOU Quan Shafiq ur Rehman ZHOU Yu WEI Xin WANG Lei ZHENG Baoyu Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Department of Applied Physics and Electronics Ume University School of Computer Science Beijing University of Posts and Telecommunications

This paper addresses face recognition problem in a more challenging scenario where the training and test samples are both subject to the visual variations of poses, expressions and misalignments. We employ dense Scale-invariant feature transform(SIFT) feature matching as a generic transformation to roughly align training samples; and then identify input facial images via an improved sparse representation model based on the aligned training samples. Compared with previous methods, the extensive experimental results demonstrate the effectiveness of our method for the task of face recognition on three benchmark datasets.

关键词： face recognition Dense SIFT feature alignment Sparse representation

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face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

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Frontiers of Computer Science 2016年第6期10卷 1118-1129页

作者： Qicong WANG Binbin WANG Xinjie HAO Lisheng CHEN Jingmin CUI Rongrong JI Yunqi LEI Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen 361005 China

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

关键词： face recognition global feature local feature,linear discriminant analysis local binary pattern decision fusion

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Optimizing Deep Neural Networks for face recognition to Increase Training Speed and Improve Model Accuracy

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Intelligent Automation & Soft Computing 2023年第12期38卷 315-332页

作者： Mostafa Diba Hossein Khosravi Faculty of Electrical Engineering Shahrood University of TechnologyShahroodSemnanP.O.Box ***Iran

Convolutional neural networks continually evolve to enhance accuracy in addressing various problems,leading to an increase in computational cost and model size.This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks.The proposed method identifies and removes inefficient filters based on the information volume in feature maps.In each layer,some feature maps lack useful information,and there exists a correlation between certain feature maps.Filters associated with these two types of feature maps impose additional computational costs on the model.By eliminating filters related to these categories of feature maps,the reduction of both computational cost and model size can be achieved.The approach employs a combination of correlation analysis and the summation of matrix elements within each feature map to detect and eliminate inefficient filters.The method was applied to two face recognition models utilizing the VGG16 and ResNet50V2 backbone architectures.In the proposed approach,the number of filters removed in each layer varies,and the removal process is independent of the adjacent layers.The convolutional layers of both backbone models were initialized with pre-trained weights from ImageNet.For training,the CASIA-Webface dataset was utilized,and the Labeled faces in the Wild(LFW)dataset was employed for benchmarking purposes.In the VGG16-based face recognition model,a 0.74%accuracy improvement was achieved while reducing the number of convolution parameters by 26.85%and decreasing Floating-point operations per second(FLOPs)by 47.96%.For the face recognition model based on the ResNet50V2 architecture,the Arcface method was implemented.The removal of inactive filters in this model led to a slight decrease in accuracy by 0.11%.However,it resulted in enhanced training speed,a reduction of 59.38%in convolution parameters,and a 57.29%decrease in FLOPs.

关键词： face recognition network pruning FLOPs reduction deep learning Arcface

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face recognition system based on CNN and LBP features for classifier optimization and fusion

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The Journal of China Universities of Posts and Telecommunications 2018年第1期25卷 37-47页

作者： Wu Yulin Jiang Mingyan School of Information Science and Engineering Shandong University

face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network （CNN） and local binary pattern （LBP） can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine （SVM） optimized by artificial bee colony （ABC） algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory （ORL） and face recognition technology （FERET） databases show the superiority of the proposed approaches.

关键词： CNN features LBP features classifier optimization fusion system face recognition

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face recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

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Computers, Materials & Continua 2021年第8期68卷 1637-1659页

作者： Masoud Muhammed Hassan Haval Ismael Hussein Adel Sabry Eesa Ramadhan J.Mstafa Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhok42002Kurdistan RegionIraq Scientific Research and Development Center Nawroz UniversityDuhok42001Kurdistan RegionIraq

Over the past few decades,face recognition has become the most effective biometric technique in recognizing people’s identity,as it is widely used in many areas of our daily lives.However,it is a challenging technique since facial images vary in rotations,expressions,and illuminations.To minimize the impact of these challenges,exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features.Therefore,this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction,Fast Independent Component Analysis(FastICA),and Linear Discriminant Analysis(LDA).In the presented method,first,face images are transformed to grayscale and resized to have a uniform size.After that,facial features are extracted from the aligned face image using Gabor,FastICA,and LDA methods.Finally,the nearest distance classifier is utilized to recognize the identity of the individuals.Here,the performance of six distance classifiers,namely Euclidean,Cosine,Bray-Curtis,Mahalanobis,Correlation,and Manhattan,are investigated.Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets:ORL,GT,FEI,and Yale.Moreover,it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.

关键词： Artificial intelligence face recognition FastICA Gabor filter bank LDA

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face recognition Based on Support Vector Machine and Nearest Neighbor Classifier

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Journal of Systems Engineering and Electronics 2003年第3期14卷 73-76页

作者： Zhang Yankun & Liu Chongqing Institute of Image Processing and Pattern recognition, Shanghai Jiao long University, Shanghai 200030 P.R.China Institute of Image Processing and Pattern recognition Shanghai Jiao tong University Shanghai 200030 P. R. China

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-

关键词： face recognition Support vector machine Nearest neighbor classifier Principal component analysis.

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