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Nature-inspired hybrid deep learning for race detection by face shape features

International Journal of Intelligent Computing and Cybernetics 2020年第3期13卷 365-388页

作者： Asha Sukumaran Thomas Brindha Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationKumaracoilIndia Department of Information Technology Noorul Islam Centre for Higher EducationKumaracoilIndia

Purpose-The humans are gifted with the potential of recognizing others by their uniqueness,in addition with more other demographic characteristics such as ethnicity(or race),gender and age,*** the decades,a vast count of researchers had undergone in the field of psychological,biological and cognitive sciences to explore how the human brain characterizes,perceives and memorizes ***,certain computational advancements have been developed to accomplish several insights into this ***/methodology/approach-This paper intends to propose a new race detection model using face shape *** proposed model includes two key phases,namely.(a)feature extraction(b)*** feature extraction is the initial stage,where the face color and shape based features get ***,maximally stable extremal regions(MSER)and speeded-up robust transform(SURF)are extracted under shape features and dense color feature are extracted as color ***,the extracted features are huge in dimensions;they are alleviated under principle component analysis(PCA)approach,which is the strongest model for solving“curse of dimensionality”.Then,the dimensional reduced features are subjected to deep belief neural network(DBN),where the race gets ***,to make the proposed framework more effective with respect to prediction,the weight of DBNis fine tuned with a new hybrid algorithm referred as lion mutated and updated dragon algorithm(LMUDA),which is the conceptual hybridization of lion algorithm(LA)and dragonfly algorithm(DA).Findings-The performance of proposed work is compared over other state-of-the-art models in terms of accuracy and error ***,LMUDA attains high accuracy at 100th iteration with 90%of training,which is 11.1,8.8,5.5 and 3.3%better than the performance when learning percentage(LP)550%,60%,70%,and 80%,*** particularly,the performance of proposed DBNþLMUDAis 22.2,12.5 and 33.3%better than the trad

关键词： Facial race detection color dense features DBN Optimization LMUDA

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