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Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features
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作者 Raqinah Alrabiah Muhammad Hussain Hatim A.AboAlSamh 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期2941-2962,共22页
The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications(e.g.,sur-veillance and human–computer interaction[HCI]).Images of varying levels ... The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications(e.g.,sur-veillance and human–computer interaction[HCI]).Images of varying levels of illumination,occlusion,and other factors are captured in uncontrolled environ-ments.Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances,and faces may be covered by a scarf,hijab,or mask due to the COVID-19 pandemic.The periocular region is a reliable source of information because it features rich discriminative biometric features.However,most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environ-ments.Motivated by the superior performance of deep learning,we proposed a new method,PeriGender,inspired by the design principles of the ResNet and DenseNet models,that can classify gender using features from the periocular region.The proposed system utilizes a dense concept in a residual model.Through skip connections,it reuses features on different scales to strengthen dis-criminative features.Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods.It achieved 87.37%,94.90%,94.14%,99.14%,and 95.17%accuracy on the GROUPS,UFPR-Periocular,Ethnic-Ocular,IMP,and UBIPr datasets,respectively,in the open-world(OW)protocol.It further achieved 97.57%and 93.20%accuracy for adult periocular images from the GROUPS dataset in the closed-world(CW)and OW protocols,respectively.The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features,and feminine features can be identified through the eyebrow,upper eyelids,and corners of the eyes.Furthermore,using a whole region without cropping enhances PeriGender’s learning capability,improving its understanding of both eyes’global structure without discontinuity. 展开更多
关键词 Gender recognition periocular region deep learning convolutional neural network unconstrained environment
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Nonmonotone Adaptive Trust Region Algorithms with Indefinite Dogleg Path for Unconstrained Minimization 被引量:13
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作者 陈俊 孙文瑜 《Northeastern Mathematical Journal》 CSCD 2008年第1期19-30,共12页
In this paper, we combine the nonmonotone and adaptive techniques with trust region method for unconstrained minimization problems. We set a new ratio of the actual descent and predicted descent. Then, instead of the ... In this paper, we combine the nonmonotone and adaptive techniques with trust region method for unconstrained minimization problems. We set a new ratio of the actual descent and predicted descent. Then, instead of the monotone sequence, the nonmonotone sequence of function values are employed. With the adaptive technique, the radius of trust region △k can be adjusted automatically to improve the efficiency of trust region methods. By means of the Bunch-Parlett factorization, we construct a method with indefinite dogleg path for solving the trust region subproblem which can handle the indefinite approximate Hessian Bk. The convergence properties of the algorithm are established. Finally, detailed numerical results are reported to show that our algorithm is efficient. 展开更多
关键词 nonmonotone trust region method adaptive method indefinite dogleg path unconstrained minimization global convergence superlinear convergence
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GLOBAL COVERGENCE OF THE NON-QUASI-NEWTON METHOD FOR UNCONSTRAINED OPTIMIZATION PROBLEMS 被引量:6
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作者 Liu Hongwei Wang Mingjie +1 位作者 Li Jinshan Zhang Xiangsun 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2006年第3期276-288,共13页
In this paper, the non-quasi-Newton's family with inexact line search applied to unconstrained optimization problems is studied. A new update formula for non-quasi-Newton's family is proposed. It is proved that the ... In this paper, the non-quasi-Newton's family with inexact line search applied to unconstrained optimization problems is studied. A new update formula for non-quasi-Newton's family is proposed. It is proved that the constituted algorithm with either Wolfe-type or Armijotype line search converges globally and Q-superlinearly if the function to be minimized has Lipschitz continuous gradient. 展开更多
关键词 non-quasi-Newton method inexact line search global convergence unconstrained optimization superlinear convergence.
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Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization 被引量:3
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作者 Chunfeng Wang Sanyang Liu Mingmin Zhu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期784-790,共7页
Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony opt... Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization(U-ACO-B) to solve the drawbacks of the ant colony optimization(ACO-B).In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm. 展开更多
关键词 Bayesian network structure learning ant colony optimization unconstrained optimization
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A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION 被引量:1
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作者 Peng Yehui Liu Zhenhai 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2005年第4期491-498,共8页
In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the sea... In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don't bear. 展开更多
关键词 unconstrained optimization pattern search method genetic algorithm global minimizer.
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A Filled Function with Adjustable Parameters for Unconstrained Global Optimization 被引量:1
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作者 SHANGYou-lin LIXiao-yan 《Chinese Quarterly Journal of Mathematics》 CSCD 2004年第3期232-239,共8页
A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two a... A filled function with adjustable parameters is suggested in this paper for finding a global minimum point of a general class of nonlinear programming problems with a bounded and closed domain. This function has two adjustable parameters. We will discuss the properties of the proposed filled function. Conditions on this function and on the values of parameters are given so that the constructed function has the desired properties of traditional filled function. 展开更多
关键词 filled function global optimization global minimizer unconstrained problem BASIN HILL
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A New Nonlinear Conjugate Gradient Method for Unconstrained Optimization Problems 被引量:1
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作者 LIU Jin-kui WANG Kai-rong +1 位作者 SONG Xiao-qian DU Xiang-lin 《Chinese Quarterly Journal of Mathematics》 CSCD 2010年第3期444-450,共7页
In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wol... In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP+ method,so it can be widely used in scientific computation. 展开更多
关键词 unconstrained optimization conjugate gradient method strong Wolfe line search sufficient descent property global convergence
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Subspace Minimization Conjugate Gradient Method Based on Cubic Regularization Model for Unconstrained Optimization 被引量:1
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作者 Ting Zhao Hongwei Liu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2021年第5期61-69,共9页
Many methods have been put forward to solve unconstrained optimization problems,among which conjugate gradient method(CG)is very important.With the increasing emergence of large⁃scale problems,the subspace technology ... Many methods have been put forward to solve unconstrained optimization problems,among which conjugate gradient method(CG)is very important.With the increasing emergence of large⁃scale problems,the subspace technology has become particularly important and widely used in the field of optimization.In this study,a new CG method was put forward,which combined subspace technology and a cubic regularization model.Besides,a special scaled norm in a cubic regularization model was analyzed.Under certain conditions,some significant characteristics of the search direction were given and the convergence of the algorithm was built.Numerical comparisons show that for the 145 test functions under the CUTEr library,the proposed method is better than two classical CG methods and two new subspaces conjugate gradient methods. 展开更多
关键词 cubic regularization model conjugate gradient method subspace technique unconstrained optimization
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A Line Search Algorithm for Unconstrained Optimization 被引量:1
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作者 Gonglin Yuan Sha Lu Zengxin Wei 《Journal of Software Engineering and Applications》 2010年第5期503-509,共7页
It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under weak conditions, thi... It is well known that the line search methods play a very important role for optimization problems. In this paper a new line search method is proposed for solving unconstrained optimization. Under weak conditions, this method possesses global convergence and R-linear convergence for nonconvex function and convex function, respectively. Moreover, the given search direction has sufficiently descent property and belongs to a trust region without carrying out any line search rule. Numerical results show that the new method is effective. 展开更多
关键词 LINE SEARCH unconstrained Optimization GLOBAL CONVERGENCE R-linear CONVERGENCE
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A CLASS OF COLLINEAR SCALING ALGORITHMS FOR UNCONSTRAINED OPTIMIZATON
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作者 盛松柏 《Numerical Mathematics A Journal of Chinese Universities(English Series)》 SCIE 1997年第2期219-230,共12页
A Class of Collinear Scaling Algorithms for Unconstrained Optimization. An appealing approach to the solution of nonlinear optimization problems based on conic models of the objective function has been in troduced by ... A Class of Collinear Scaling Algorithms for Unconstrained Optimization. An appealing approach to the solution of nonlinear optimization problems based on conic models of the objective function has been in troduced by Davidon (1980). It leads to a broad class of algorithms which can be considered to generalize the existing quasi-Newton methods. One particular member of this class has been deeply discussed by Sorensen (1980), who has proved some interesting theoretical properties. In this paper, we generalize Sorensen’s technique to Spedicato three-parameter family of variable-metric updates. Furthermore, we point out that the collinear scaling three- parameter family is essentially equivalent to the Spedicato three-parameter family. In addition, numerical expriments have been carried out to compare some colliner scaling algorithms with a straightforward implementation of the BFGS quasi-Newton method. 展开更多
关键词 unconstrained optimization CONIC models COLLINEAR scaling QUASI-NEWTON algorithms.
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New type of conjugate gradient algorithms for unconstrained optimization problems
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作者 Caiying Wu Guoqing Chen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第6期1000-1007,共8页
Two new formulaes of the main parameter βk of the conjugate gradient method are presented, which respectively can be seen as the modifications of method HS and PRP. In comparison with classic conjugate gradient metho... Two new formulaes of the main parameter βk of the conjugate gradient method are presented, which respectively can be seen as the modifications of method HS and PRP. In comparison with classic conjugate gradient methods, the new methods take both available gradient and function value information. Furthermore, their modifications are proposed. These methods are shown to be global convergent under some assumptions. Numerical results are also reported. 展开更多
关键词 conjugate gradient unconstrained optimization global convergence conjugacy condition.
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Modified LS Method for Unconstrained Optimization
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作者 Jinkui Liu Li Zheng 《Applied Mathematics》 2011年第6期779-782,共4页
In this paper, a new conjugate gradient formula and its algorithm for solving unconstrained optimization problems are proposed. The given formula satisfies with satisfying the descent condition. Under the Grippo-Lucid... In this paper, a new conjugate gradient formula and its algorithm for solving unconstrained optimization problems are proposed. The given formula satisfies with satisfying the descent condition. Under the Grippo-Lucidi line search, the global convergence property of the given method is discussed. The numerical results show that the new method is efficient for the given test problems. 展开更多
关键词 unconstrained Optimization CONJUGATE GRADIENT Method Grippo-Lucidi Line SEARCH Global CONVERGENCE
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A Retrospective Filter Trust Region Algorithm for Unconstrained Optimization
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作者 Yue Lu Zhongwen Chen 《Applied Mathematics》 2010年第3期179-188,共10页
In this paper, we propose a retrospective filter trust region algorithm for unconstrained optimization, which is based on the framework of the retrospective trust region method and associated with the technique of the... In this paper, we propose a retrospective filter trust region algorithm for unconstrained optimization, which is based on the framework of the retrospective trust region method and associated with the technique of the multi-dimensional filter. The new algorithm gives a good estimation of trust region radius, relaxes the condition of accepting a trial step for the usual trust region methods. Under reasonable assumptions, we analyze the global convergence of the new method and report the preliminary results of numerical tests. We compare the results with those of the basic trust region algorithm, the filter trust region algorithm and the retrospective trust region algorithm, which shows the effectiveness of the new algorithm. 展开更多
关键词 unconstrained Optimization RETROSPECTIVE TRUST Region Method MULTI-DIMENSIONAL FILTER Technique
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A New Descent Nonlinear Conjugate Gradient Method for Unconstrained Optimization
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作者 Hao Fan Zhibin Zhu Anwa Zhou 《Applied Mathematics》 2011年第9期1119-1123,共5页
In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization. The sufficient descent property holds without any line searches. We use some steplength technique which ... In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization. The sufficient descent property holds without any line searches. We use some steplength technique which ensures the Zoutendijk condition to be held, this method is proved to be globally convergent. Finally, we improve it, and do further analysis. 展开更多
关键词 Large Scale unconstrained Optimization CONJUGATE Gradient Method SUFFICIENT DESCENT Property Globally CONVERGENT
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Solving Large Scale Unconstrained Minimization Problems by a New ODE Numerical Integration Method
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作者 Tianmin Han Xinlong Luo Yuhuan Han 《Applied Mathematics》 2011年第5期527-532,共6页
In reference [1], for large scale nonlinear equations , a new ODE solving method was given. This paper is a continuous work. Here has gradient structure i.e. , is a scalar function. The eigenvalues of the Jacobian of;... In reference [1], for large scale nonlinear equations , a new ODE solving method was given. This paper is a continuous work. Here has gradient structure i.e. , is a scalar function. The eigenvalues of the Jacobian of;or the Hessian of , are all real number. So the new method is very suitable for this structure. For quadratic function the convergence was proved and the spectral radius of iteration matrix was given and compared with traditional method. Examples show for large scale problems (dimension ) the new method is very efficient. 展开更多
关键词 unconstrained MINIMIZATION Problem Gradient EQUATIONS QUADRATIC Model Spectral RADIUS ODE Numerical Integration
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On the Global Convergence of the PERRY-SHANNO Method for Nonconvex Unconstrained Optimization Problems
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作者 Linghua Huang Qingjun Wu Gonglin Yuan 《Applied Mathematics》 2011年第3期315-320,共6页
In this paper, we prove the global convergence of the Perry-Shanno’s memoryless quasi-Newton (PSMQN) method with a new inexact line search when applied to nonconvex unconstrained minimization problems. Preliminary nu... In this paper, we prove the global convergence of the Perry-Shanno’s memoryless quasi-Newton (PSMQN) method with a new inexact line search when applied to nonconvex unconstrained minimization problems. Preliminary numerical results show that the PSMQN with the particularly line search conditions are very promising. 展开更多
关键词 unconstrained OPTIMIZATION NONCONVEX OPTIMIZATION GLOBAL CONVERGENCE
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An Improved Quasi-Newton Method for Unconstrained Optimization
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作者 Fei Pusheng Chen Zhong (Department of Mathematics, Wuhan University, Wuhan 430072, China) 《Wuhan University Journal of Natural Sciences》 CAS 1996年第1期35-37,共3页
We present an improved method. If we assume that the objective function is twice continuously differentiable and uniformly convex, we discuss global and superlinear convergence of the improved quasi-Newton method.
关键词 quasi-Newton method superlinear convergence unconstrained optimization
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A Non-Monotone Trust Region Method with Non-Monotone Wolfe-Type Line Search Strategy for Unconstrained Optimization
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作者 Changyuan Li Qinghua Zhou Xiao Wu 《Journal of Applied Mathematics and Physics》 2015年第6期707-712,共6页
In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our alg... In this paper, we propose and analyze a non-monotone trust region method with non-monotone line search strategy for unconstrained optimization problems. Unlike the traditional non-monotone trust region method, our algorithm utilizes non-monotone Wolfe line search to get the next point if a trial step is not adopted. Thus, it can reduce the number of solving sub-problems. Theoretical analysis shows that the new proposed method has a global convergence under some mild conditions. 展开更多
关键词 unconstrained Optimization Non-Monotone TRUST Region Method Non-Monotone Line Search Global Convergence
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New Variants of Newton’s Method for Nonlinear Unconstrained Optimization Problems
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作者 V. KANWAR Kapil K. SHARMA Ramandeep BEHL 《Intelligent Information Management》 2010年第1期40-45,共6页
In this paper, we propose new variants of Newton’s method based on quadrature formula and power mean for solving nonlinear unconstrained optimization problems. It is proved that the order of convergence of the propos... In this paper, we propose new variants of Newton’s method based on quadrature formula and power mean for solving nonlinear unconstrained optimization problems. It is proved that the order of convergence of the proposed family is three. Numerical comparisons are made to show the performance of the presented methods. Furthermore, numerical experiments demonstrate that the logarithmic mean Newton’s method outperform the classical Newton’s and other variants of Newton’s method. MSC: 65H05. 展开更多
关键词 unconstrained optimization Newton’s method order of CONVERGENCE power MEANS INITIAL GUESS
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Global Convergence of an Extended Descent Algorithm without Line Search for Unconstrained Optimization
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作者 Cuiling Chen Liling Luo +1 位作者 Caihong Han Yu Chen 《Journal of Applied Mathematics and Physics》 2018年第1期130-137,共8页
In this paper, we extend a descent algorithm without line search for solving unconstrained optimization problems. Under mild conditions, its global convergence is established. Further, we generalize the search directi... In this paper, we extend a descent algorithm without line search for solving unconstrained optimization problems. Under mild conditions, its global convergence is established. Further, we generalize the search direction to more general form, and also obtain the global convergence of corresponding algorithm. The numerical results illustrate that the new algorithm is effective. 展开更多
关键词 unconstrained Optimization DESCENT Method Line SEARCH Global CONVERGENCE
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