A TWO-STAGE SYNERGETIC APPROACH FOR FACE RECOGNITION

2004 ◽  
Vol 18 (06) ◽  
pp. 1007-1017
Author(s):  
WEIGANG CHEN ◽  
FEIHU QI ◽  
ZHAOZHONG WANG
Keyword(s):  
Face Recognition ◽  
Potential Function ◽  
Order Parameter ◽  
Test Pattern ◽  
Face Image ◽  
Recognition Method ◽  
Two Stage ◽  
Second Stage ◽  
Definition Of ◽  
Global Similarity

A two-stage face recognition method is presented in this paper. In the first stage, the set of candidate patterns is narrowed down with the global similarity being taken into account. In the second stage, synergetic approach is employed to perform further recognition. Face image is segmented into meaningful regions, each of which is represented as a prototype vector. The similarity between a given region of the test pattern and a stored sample is obtained as the order parameter which serves as an element of the order vector. Finally, a modified definition of the potential function is given, and the dynamic model of recognition is derived from it. The effectiveness of the proposed method is experimentally confirmed.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

The Two-Stage Recognition Method Based on Texture Signals of the Heterogeneous Unsteady Iris

2021 ◽  
Author(s):  
Shuai Liu ◽  
Yuanning Liu ◽  
Xiaodong Zhu ◽  
Jing Liu ◽  
Guang Huo ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Recognition Accuracy ◽  
Texture Features ◽  
Unsteady State ◽  
Recognition Method ◽  
Two Stage ◽  
Second Stage ◽  
Highly Efficient ◽  
Training Samples ◽  
Gray Values

In this paper, a two-stage multi-category recognition structure based on texture features is proposed. This method can solve the problem of the decline in recognition accuracy in the scene of lightweight training samples. Besides, the problem of recognition effect different in the same recognition structure caused by the unsteady iris can also be solved. In this paper’s structure, digitized values of the edge shape in the iris texture of the image are set as the texture trend feature, while the differences between the gray values of the image obtained by convolution are set as the grayscale difference feature. Furthermore, the texture trend feature is used in the first-stage recognition. The template category that does not match the tested iris is the elimination category, and the remaining categories are uncertain categories. Whereas, in the second-stage recognition, uncertain categories are adopted to determine the iris recognition conclusion through the grayscale difference feature. Then, the experiment results using the JLU iris library show that the method in this paper can be highly efficient in multi-category heterogeneous iris recognition under lightweight training samples and unsteady state.

scholarly journals The Face Recognition Method Based on CS-LBP and DBN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Sun ◽  
Xin Yin ◽  
Mingxin Yang ◽  
Yang Wang ◽  
Jianying Fan
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Face Image ◽  
Recognition Method ◽  
Traditional Methods ◽  
Face Images ◽  
The Face

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

The Research of Face Recognition Method Based on Wavelet Transform

2012 ◽  
Vol 442 ◽  
pp. 463-467
Author(s):  
Li Hong Bi ◽  
Yan Fang Ma ◽  
Li Hua Piao
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Biometric Identification ◽  
Recognition Method ◽  
Image Block ◽  
The Face ◽  
Speed And Accuracy ◽  
Great Development

Face recognition is a kind of biometric identification technology possessing great development potential, researching on this technology has great theoretical values. The paper presents a method of image block principal component analysis (PCA) based on wavelet transform. The image was firstly disposed by wavelet transform and segmented, then we set the different weight values for each of parts according to the different role in the overall image and extract eigenvector using the PCA, finally, the face image is recognized according to the eigenvector and feature. This method can improve the speed and accuracy, reduce the complexity of feature extraction and improve the speed of recognition.

Human Face Recognition Based on the Core Characteristics under the Condition of Illumination

2014 ◽  
Vol 484-485 ◽  
pp. 991-995
Author(s):  
Wen Hui Li ◽  
Ning Ma ◽  
Zhi Yan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Result ◽  
Support Vector ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Core ◽  
Core Characteristics

A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of light. With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine (SVM) based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.

scholarly journals The commercialization instruments of innovations: empirical research on FMCG market

2018 ◽  
pp. 42-51 ◽  
Author(s):  
E. V. Arsenova ◽  
O. N. Pankova
Keyword(s):  
Empirical Research ◽  
Market Research ◽  
New Products ◽  
Special Importance ◽  
Practical Application ◽  
Two Stage ◽  
Russian Market ◽  
Case Research ◽  
Second Stage ◽  
Definition Of

The research objective is in definition of the main tools participating in process of commercialization of grocery and marketing on the basis of expansion and deepening of the existing theory and methodology of commercialization, and also development of recommendations to improvement of tools that will promote finally to increase of efficiency of process of acceptance of a novelty by the market. Research is conducted on the basis of two-stage methodology including at the first stage carrying out questioning among the employees occupied in the course of commercialization in the large companies FMCG of the market, and at the second stage a case - research of two - players of the Russian market of juice. Confirmation of the made hypothesis that in most cases the innovations put on the market, can be considered grocery that is explainable that such innovations are capable to give to consumers essentially new advantages is result of research. The interrelation between stages of commercialization and the tools applied during this period is also confirmed. In the analysis of separate tools the tools informing the consumer on new products showed the greatest importance. Among them the special importance is played by TV advertizing. Practical application of results consists in identification tools for each of stages of commercialization of innovations the FMCG companies, and also those from them which play a paramount role on each of stages are defined.

scholarly journals Open Set Sheep Face Recognition Based on Euclidean Space Metric

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongcheng Xue ◽  
Junping Qin ◽  
Chao Quan ◽  
Wei Ren ◽  
Tong Gao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Animal Husbandry ◽  
Face Image ◽  
Recognition Method ◽  
Data Set ◽  
Identity Recognition ◽  
Identity Information ◽  
Face Area ◽  
Face Images ◽  
Open Set

As the essential content of intelligent animal husbandry, identifying each livestock is the only way to achieve modern and refined scientific husbandry. This paper proposes a sheep face recognition method based on European spatial metrics and realizes noncontact sheep identity recognition by training the network using sheep face image samples in the natural environment. The SheepBase data set was first proposed in this process, which contains 6559 images of Inner Mongolia fine-wool sheep and Sunite sheep. To enhance the diversity of the data, the sheep face images were data-enhanced. Secondly, to solve the problems of more redundant information in the sheep face image and the poor posture and angle of the sheep face, we propose the sheep face detection and correction (SheepFaceRepair) method. This method aims to detect the sheep face area in the image to be recognized and align the sheep face area. On this basis, we offer an open sheep facial recognition network (SheepFaceNet) based on the European spatial metric. This method incorporates the biological identity information features of the sheep face to achieve sheep identity. We also tested the effectiveness of this method in the SheepBase data set. The experimental results show that the method proposed in this paper is much higher than the other methods, and the precision of recognition reaches 89.12%. In addition, we found that integrating the biometrics of the sheep face can effectively improve the network’s recognition capacity.

Face recognition method based on DLPP-SIFT

2013 ◽  
Vol 32 (9) ◽  
pp. 2588-2591
Author(s):  
Zheng-yi LI ◽  
Gui-yu FENG ◽  
Long ZHAO
Keyword(s):  
Face Recognition ◽  
Recognition Method
Sign in / Sign up

Export Citation Format

Share Document