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A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation 被引量:1
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作者 Mohamed Karray Nesrine Triki Mohamed Ksantini 《Computers, Materials & Continua》 SCIE EI 2024年第7期119-138,共20页
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn... Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology. 展开更多
关键词 Driving automation advanced driver assistance systems(ADAS) traffic sign recognition(TSR) artificial intelligence ensemble learning belief functions voting method
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A High-Accuracy Curve Boundary Recognition Method Based on the Lattice Boltzmann Method and Immersed Moving Boundary Method
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作者 Jie-Di Weng Yong-Zheng Jiang +2 位作者 Long-Chao Chen Xu Zhang Guan-Yong Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2533-2557,共25页
Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Latti... Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Lattice Boltzmann(LBM)and the immersion boundary method based on solid ratio(IMB)have limitations in identifying custom curved boundaries.Meanwhile,IBM based on velocity correction(IBM-VC)suffers from inaccuracies and numerical instability.Therefore,this study introduces a high-accuracy curve boundary recognition method(IMB-CB),which identifies boundary nodes by moving the search box,and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,achieving accurate recognition of custom curve boundaries.In addition,curve boundary image and dot methods are utilized to verify IMB-CB.The findings revealed that IMB-CB can accurately identify the boundary,showing an error of less than 1.8%with 500 lattices.Also,the flow in the custom curve boundary and aerodynamic characteristics of the NACA0012 airfoil are calculated and compared to IBM-VC.Results showed that IMB-CB yields lower lift and drag coefficient errors than IBM-VC,with a 1.45%drag coefficient error.In addition,the characteristic curve of IMB-CB is very stable,whereas that of IBM-VC is not.For the moving boundary problem,LBM-IMB-CB with discrete element method(DEM)is capable of accurately simulating the physical phenomena of multi-moving particle flow in complex curved pipelines.This research proposes a new curve boundary recognition method,which can significantly promote the stability and accuracy of fluid-solid interaction simulations and thus has huge applications in engineering. 展开更多
关键词 Fluid-solid interaction curve boundary recognition method Lattice Boltzmann method immersed moving boundary method
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A Deep Transfer Learning Approach for Addressing Yaw Pose Variation to Improve Face Recognition Performance
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作者 M.Jayasree K.A.Sunitha +3 位作者 A.Brindha Punna Rajasekhar G.Aravamuthan G.Joselin Retnakumar 《Intelligent Automation & Soft Computing》 2024年第4期745-764,共20页
Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for d... Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses. 展开更多
关键词 Face recognition pose variations transfer learning method yaw poses FaceNet Inception-v2
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YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module
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作者 Zhangchi Liu Risheng Zhang +1 位作者 Hao Zhong Yingjie Sun 《Open Journal of Applied Sciences》 2024年第1期159-170,共12页
The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Con... The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods. 展开更多
关键词 Object recognition CBAM WioU State-of-the-Art methods
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A Study of the Method for the Recognition of Anomalies in Geochemical Hydrocarbon Exploration 被引量:2
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作者 Zhang Liuping Doctoral Student, China University of Geosciences, Wuhan 430074 Liao Zebin North China Institute of Petroleum Exploration and Development, Renqiu 062552 《Journal of Earth Science》 SCIE CAS CSCD 1998年第1期74-82,共9页
The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomal... The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomalies from lateral anomalies controlled by faults; and (3) how to eliminate interferences. These uncertainties are serious obstacles for the wide acceptance and use of geochemical techniques in hydrocarbon exploration. In this paper, the features of hydrocarbon anomalies were analyzed based on the micro migration mechanisms. In most cases, there are two anomalous populations or point groups, which are produced by two distinct mechanisms: (1) a population that directly reflects oil and gas fields, and (2) one that is related to structures such as faults. Statistical studies show that background anomalous populations and the boundaries between them can be described by the population means, prior probabilities, which are the proportions of population sizes, and covariance matrices, when background and anomalous populations have normal distributions. When this normality condition is met, a series of formulas can be derived. The method is designed on the basis of these allows: (1) univariate anomaly recognition, (2) elimination of interferences, (3) multivariate anomaly recognition, and (4) multivariate anomaly combination which depicts a more representative picture of morphology of the anomalous target than individual anomalies. The univariate and multivariate anomaly recognition can not only separate anomalies from background objectively, but also simultaneously distinguish the two types of anomalies objectively. This method was applied to the hydrocarbon data in Yangshuiwu region, Hebei Province. The interferences from regional variation of background were eliminated, and the interpretation uncertainty was reduced greatly as the anomalous populations were separated. The method was also used in Daxing region within the confines of Beijing City, and Aershan and Jiergalangtu regions in Inner Mongolia. 展开更多
关键词 geochemical exploration petroleum exploration ANOMALY recognition data processing method research.
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Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area 被引量:4
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作者 MAO Yuan-yuan ZHANG Xue-gang WANG Lian-sheng 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第11期1917-1922,共6页
Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollutio... Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization method reflects more efficiently the fuzzy nature of the groundwater vulnerability to pollution and is more applicable in reality. 展开更多
关键词 Fuzzy sets recognition and optimization method Groundwater vulnerability to pollution DRASTIC
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New Image Recognition Method Based on Rough-Sets and Fuzzy Theory 被引量:1
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作者 张艳 李凤霞 战守义 《Journal of Beijing Institute of Technology》 EI CAS 2003年第3期255-259,共5页
A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that... A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough sets method when used alone. 展开更多
关键词 fuzzy method rough sets theory image recognition
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Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition 被引量:2
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作者 Hao Zheng Jianfang Liu Mengyi Liao 《Journal of Computer and Communications》 2021年第1期10-18,共9页
In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only ... In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition. 展开更多
关键词 YOLOv3 Local Optical Flow method Human Behavior recognition
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Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha 被引量:1
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作者 Tessfu Geteye Fantaye Junqing Yu Tulu Tilahun Hailu 《Journal of Signal and Information Processing》 2020年第1期1-21,共21页
Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency a... Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems. 展开更多
关键词 Automatic SPEECH recognition MULTILINGUAL DNN Modeling methods Basic PHONE ACOUSTIC UNITS Rounded PHONE ACOUSTIC UNITS Chaha
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Feasibility study of the transient electromagnetic method for chamber blasting misfire detection and recognition 被引量:1
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作者 Liu Liansheng Liang Longhua +2 位作者 Wu Jiyang Jiao Yongbin Lu Zhexiang 《Engineering Sciences》 EI 2014年第6期111-116,共6页
In this paper,transient electromagnetic method was used to carry out the feasibility study on the detection and recognition of chamber blasting misfire.Firstly,an electromagnetic background field was established in th... In this paper,transient electromagnetic method was used to carry out the feasibility study on the detection and recognition of chamber blasting misfire.Firstly,an electromagnetic background field was established in the test;secondly,a benign conductor was preset in the chamber,and then the background field was eliminated after the electromagnetic field was measured;thirdly,the transient electromagnetic field was measured again after blasting;at last,the chamber blasting misfire was detected and recognized by comparing the change of eddy current field of the preset benign conductor before and after blasting.The test results showed that:When the buried depth of aluminum box target was no more than 30 m,transient electromagnetic method can clearly identify the position of the aluminum box;when the buried depth of aluminum box was more than30 m,the buried depth and position of the aluminum box was not sure due to the unknown level of secondary eddy current field generated by aluminum box. 展开更多
关键词 transient electromagnetic methods chamber blasting misfire detection and recognition eddy cur- rent field TARGET
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Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics
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作者 Siwei Li Liang Yue +1 位作者 Xiangyu Kong Chengshan Wang 《Global Energy Interconnection》 EI CSCD 2024年第3期313-323,共11页
This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online ide... This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. 展开更多
关键词 Load aggregation Regional large-scale Online recognition Feature extraction method
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Analysis of PCA Method in Image Recognition with MATALAB
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作者 ZHAO Ping 《枣庄学院学报》 2014年第4期124-126,共3页
The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but stil... The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program. 展开更多
关键词 ANALYSIS PCA method IMAGE recognition MATLAB
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A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm
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作者 Qiu Chen Koji Kotani +1 位作者 Feifei Lee Tadahiro Ohmi 《Journal of Software Engineering and Applications》 2010年第2期119-124,共6页
In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, first... In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition. 展开更多
关键词 FACE recognition Vector QUANTIZATION (VQ) CODEBOOK Design Code Classification HISTOGRAM method
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Fault Pattern Recognition based on Kernel Method and Fuzzy C-means
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作者 SUN Yebei ZHAO Rongzhen TANG Xiaobin 《International Journal of Plant Engineering and Management》 2016年第4期231-240,共10页
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c... A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means (FCM) is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery. 展开更多
关键词 Kernel method fuzzy C-means FCM pattern recognition CLUSTERING
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The Methodological Connotation of Constructing Socialist Road Recognition with Chinese Characteristics at Present
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作者 Meng Xiangfei 《学术界》 CSSCI 北大核心 2018年第5期237-249,共13页
关键词 道路识别 社会 中国 主义 构造 特征 方法学 涵义
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2DPCA versus PCA for face recognition 被引量:5
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作者 胡建军 谭冠政 +1 位作者 栾凤刚 A.S.M.LIBDA 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第5期1809-1816,共8页
Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. ... Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim. 展开更多
关键词 face recognition dimensionality reduction 2DPCA method PCA method column-image difference(CID)
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An attribute recognition model for safe thickness assessment between concealed karst cave and tunnel 被引量:12
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作者 HUANG Xin LI Shu-cai +5 位作者 XU Zhen-hao GUO Ming SHI Xue-song GAO Bin ZHANG Bo LIU Lang 《Journal of Central South University》 SCIE EI CAS CSCD 2019年第4期955-969,共15页
An attribute recognition model for safe thickness assessment between a concealed karst cave and a tunnel is established based on the attribute mathematic theory.The model can be applied to carrying out risk classifica... An attribute recognition model for safe thickness assessment between a concealed karst cave and a tunnel is established based on the attribute mathematic theory.The model can be applied to carrying out risk classification of the safe thickness between a concealed karst cave and a tunnel and to guarantee construction’s safety in tunnel engineering.Firstly,the assessment indicators and classification standard of safe thickness between a concealed karst cave and a tunnel are studied based on the perturbation method.Then some attribute measurement functions are constructed to compute the attribute measurement of each single index and synthetic attribute measurement.Finally,the identification and classification of risk assessment of safe thickness between a concealed karst cave and a tunnel are recognized by the confidence criterion.The results of two engineering application show that the evaluation results agree well with the site situations in construction.The results provide a good guidance for the tunnel construction. 展开更多
关键词 concealed karst cave karst tunnel safe thickness attribute recognition method
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Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition 被引量:5
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作者 任春涛 李畅游 +3 位作者 贾克力 张生 李卫平 曹有玲 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2008年第3期339-344,共6页
Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clu... Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application. 展开更多
关键词 transitive closure method ISODATA clustering algorithm fuzzy pattern recognition method partitioning of water quality
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Pre-detection and dual-dictionary sparse representation based face recognition algorithm in non-sufficient training samples 被引量:2
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作者 ZHAO Jian ZHANG Chao +3 位作者 ZHANG Shunli LU Tingting SU Weiwen JIA Jian 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第1期196-202,共7页
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and pos... Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR). 展开更多
关键词 face recognition facial pose pre-recognition(FPPR) dual-dictionary sparse representation method machine learning
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RESEARCH ON TWO-DIMENSIONAL LDA FOR FACE RECOGNITION 被引量:2
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作者 Han Ke Zhu Xiuchang 《Journal of Electronics(China)》 2006年第6期943-947,共5页
The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear... The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear discriminant analysis method makes full use of not only the row and the column direc-tion information of face images but also the discriminant information among different classes. The method is evaluated using the Nanjing University of Science and Technology (NUST) 603 face database and the Aleix Martinez and Robert Benavente (AR) face database. Experimental results show that the method in the letter is feasible and effective. 展开更多
关键词 Face recognition Feature extraction Pattern recognition Subspace methods
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