Face recognition based on subset selection via metric learning on manifold

Shao, Hong; Chen, Shuang; Zhao, Jincheng; Cui, Wencheng; Yu, Tianshu

doi:10.1631/fitee.1500085

Cited by 21 publications

(11 citation statements)

References 34 publications

(58 reference statements)

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“…They design different feature descriptors for regression. They are inspired by the methods applied to human face such as face alignment [6][7][8] and face recognition [9].…”

Section: Landmarks Detection Methodsmentioning

confidence: 99%

Clothes Size Prediction from Dressed-Human Silhouettes

Song

Tong

Chang

et al. 2017

Next Generation Computer Animation Techniques

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Abstract. We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1 second and the average estimation error of body sizes is 0.824 cm, which can satisfy the tolerance for customers to shop clothes online.

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“…They design different feature descriptors for regression. They are inspired by the methods applied to human face such as face alignment [6][7][8] and face recognition [9].…”

Section: Landmarks Detection Methodsmentioning

confidence: 99%

Clothes Size Prediction from Dressed-Human Silhouettes

Song

Tong

Chang

et al. 2017

Next Generation Computer Animation Techniques

View full text Add to dashboard Cite

show abstract

“…Usually, the dimension of the data is unified by the translation to standard deviation transformation and the shift to range transformation [37][38][39][40][41], and the fuzzy matrix in fuzzy clustering is obtained. If the degree of similarity which is the coefficient of similarity [42][43][44][45][46][47][48].…”

Section: Fuzzy Cluster Analysis Modelmentioning

confidence: 99%

Location Selection of Logistics Center in e-Commerce Network Environments

Chen¹

2017

AJNNA

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Abstract:The site selection of logistics center is a very complicated and enormous system problem. Each site selection method and model is based on a certain premise and hypothesis. The site selection decision of distribution center has an important influence on the whole logistics system operation. A good location of urban logistics sites is important for optimizing the logistics network, and improving the urban traffic conditions, and level of logistics services. Motivated by this, in this paper, based on the research of sixteen cities in southeastern China and neural network algorithms, we proposed a logistics center location selection algorithm. Our method considers the six important concepts reflecting the performance index of the city logistics, such as city location quotient, market prosperity degree, proportion of freight volume, urban centricity, per capita gross domestic product (GDP), and population size. Our method conducts the nested fuzzy analytic hierarchy process (AHP), and then investigates the total ranking of the single order hierarchy to select suitable logistics centers.

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“…The analyses done on the market, competitors and ability of the company are necessary for creating a good positioning statement. Aided with this statement, one can start on creating the marketing mix [76].…”

Section: Model 6: Matrix Of Management Strategiesmentioning

confidence: 99%

SEM Statistical Analyses in Management Strategies

Ren¹,

Zeng²,

Wu³

et al. 2017

IJDMS

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This paper was to analyse what factors will affect online shoppers' purchase intention in the e-business environment. This paper discussed a number of conceptual models and following hypotheses. Two typical e-business companies (Alibaba and Amazon) were ideal comparative analysis models. Structural Equation Modelling (SEM) and the factor analysis were adopted for statistical and empirical analyses. The results showed highly positive correlations among the identified factors. The factors indicated a large innovative performance influence. The factors also verified the huge impact in the sustain ability development. These results may provide some vision on how e-business companies can win a competition and increase their profit.

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Face recognition based on subset selection via metric learning on manifold

Cited by 21 publications

References 34 publications

Clothes Size Prediction from Dressed-Human Silhouettes

Clothes Size Prediction from Dressed-Human Silhouettes

Location Selection of Logistics Center in e-Commerce Network Environments

SEM Statistical Analyses in Management Strategies

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