Purpose–The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML.Design/methodology/approach–In this paper,the authors modified the e...Purpose–The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML.Design/methodology/approach–In this paper,the authors modified the evaluation function utilized by GEFeWSML such that the weights assigned to each objective(i.e.error reduction and feature reduction)were varied.For each set of weights,GEFeWSML was used to evolve FMs for the face,periocular,and faceþperiocular templates.The best performing FMs on the training set(FMtss)and the best performing FMs on the validation set(FM*s)were then applied to the test set in order to evaluate how well they generalized to the unseen subjects.Findings–By varying the weights assigned to each of the objectives,the authors were able to suggest values that would result in the best optimization and generalization performances for facial,periocular,and faceþperiocular recognition.GEFeWSML using these suggested values outperformed the previously reported GEFeWSML results,using significantly fewer features while achieving the same recognition accuracies statistically.Originality/value–In this paper,the authors investigate the relative weighting of each objective using a value preference structure and suggest the best weights to be used for each biometric modality tested.展开更多
基金the Office of the Director of National Intelligence(ODNI)Center for Academic Excellence(CAE)for the multi-university Center for Advanced Studies in Identity Sciences(CASIS)by the National Science Foundation(NSF)Science&Technology Center:Bio/computational Evolution in Action CONsortium(BEACON).
文摘Purpose–The aim of this paper is to explore the value preference space associated with the optimization and generalization performance of GEFeWSML.Design/methodology/approach–In this paper,the authors modified the evaluation function utilized by GEFeWSML such that the weights assigned to each objective(i.e.error reduction and feature reduction)were varied.For each set of weights,GEFeWSML was used to evolve FMs for the face,periocular,and faceþperiocular templates.The best performing FMs on the training set(FMtss)and the best performing FMs on the validation set(FM*s)were then applied to the test set in order to evaluate how well they generalized to the unseen subjects.Findings–By varying the weights assigned to each of the objectives,the authors were able to suggest values that would result in the best optimization and generalization performances for facial,periocular,and faceþperiocular recognition.GEFeWSML using these suggested values outperformed the previously reported GEFeWSML results,using significantly fewer features while achieving the same recognition accuracies statistically.Originality/value–In this paper,the authors investigate the relative weighting of each objective using a value preference structure and suggest the best weights to be used for each biometric modality tested.
