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Stochastic gradient descent linear collaborative discriminant regression classification based face recognition

EVOLUTIONARY INTELLIGENCE 2022年第3期15卷 1729-1743页

作者： Akheel, T. Syed Shree, V. Usha Mastani, S. Aruna JNTUA Dept Elect & Commun Engn Anantapur Andhra Pradesh India Joginpally BR Engn Coll Dept Elect & Commun Engn Hyderabad India JNTUCEA Dept Elect & Commun Engn Anantapur Andhra Pradesh India

Facial recognition is an important research topic in biometric authentication to provide a secure access to a computer system. Due to intra and inter personal variations, facial expressions and aging variations are major concerns in facial recognition system. In order to address this concern, a new discriminant algorithm is proposed in this research article. A gradient descent with momentum and style transfer function are included in linear collaborative discriminant regression classification algorithm to characterize the dissimilar contributions of the training facial images and to learn the optimal projection matrix by maximizing the ratio of the within class reconstruction error and collaborative between class reconstruction error. Hence, the proposed collaborative algorithm not only enhance the recognition accuracy and also overall computing efficiency. In the experimental phase, the performance of the proposed stochastic gradient descent linear collaborative discriminant regression classification algorithm is validated on ORL, YALE, and extended YALE B datasets, because these databases comprise of the extensive variety of face details, expressions, and degree of scales. The experimental outcome shows that the proposed algorithm achieved maximum of 93.34%, 90.07%, and 98.83% of recognition accuracy on ORL, YALE, and extended YALE B datasets, respectively. Compared to the other existing models, the proposed algorithm approximately showed 1.5% to 10.5% improvement in face recognition in terms of recognition accuracy on ORL, YALE, and extended YALE B datasets.

关键词： collaborative representation Face recognition Gradient descent with momentum linear collaborative discriminant regression classification Style transfer function

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Gradient self-weighting linear collaborative discriminant regression classification for human cognitive states classification

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MACHINE VISION AND APPLICATIONS 2020年第3期31卷 21-21页

作者： Gupta, K. O. Chatur, P. N. Datta Meghe Inst Engn Technol & Res Dept Comp Sci & Engn Wardha 442001 Maharashtra India Govt Coll Engn Dept Comp Sci & Engn Kathora Sq Amravati 444604 Maharashtra India

In recent decades, huge volumes of data are available to inspect human brain activities for disease detection. Specifically, the functional magnetic resonance imaging (fMRI) is a powerful tool to enquire the brain functions. In fMRI, identifying the active patterns of the specific cognitive state is one of the emerging concerns for neuroscientists. The high-dimensional features make fMRI data difficult for mining and classification, because if the volume of the data space increases, then the acquired data become sparse, which leads to the "curse of dimensionality" problem. To address this concern, a new feature selection and classification methodology was proposed for classifying the human cognitive states from fMRI data. Initially, the fMRI data were collected from the StarPlus and Haxby datasets. Then, k-nearest neighbors algorithm (k-NN)-based genetic algorithm was developed to choose the optimal voxels from the active region of interests. The proposed approach selects the data to feature subsets based on k-NN algorithm, so the data volume was effectively reduced and the voxel information was maintained significantly. The most informative voxels were given as the input for gradient self-weighting that produces an optimal weight value. Respective weight value was added to the projection matrix of linear collaborative discriminant regression classification for identifying the future projection matrix that reduces the error between two individual voxels in subspace. The experimental outcome shows that the proposed methodology improved the accuracy in fMRI data classification up to 0.7-23% compared to the existing methods.

关键词： Feature selection Functional magnetic resonance imaging k-nearest neighbors linear collaborative discriminant regression classification Region of interest

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linear collaborative discriminant regression classification for face recognition

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 31卷 312-319页

作者： Qu, Xiaochao Kim, Suah Cui, Run Kim, Hyoung Joong Korea Univ Grad Sch Informat Secur & Management Seoul 136713 South Korea

This paper proposes a novel face recognition method that improves Huang's linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using linear regression classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Face recognition Feature extraction Dimensionality reduction collaborative representation Sparse representation linear regression classification linear collaborative discriminant regression classification linear discriminant regression classification

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Cognitive State classification using Genetic Algorithm based linear collaborative discriminant regression 1

Cognitive State Classification using Genetic Algorithm based...

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1st International Conference on Secure Cyber Computing and Communications (ICSCCC)

作者： Gupta, K. O. Chatur, P. N. DM Inst Engn Technol & Res Dept Comp Sci & Engn Wardha India Govt Coll Engn Dept Comp Sci & Engn Amravati India

ISBN: (纸本)9781538663738

Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this information. Main concern of all these techniques is huge dimensions of the data produced by these images. This paper proposes an efficient hybrid method for feature selection and classification. This method combine entropy based genetic algorithm (EGA) with linear collaborative discriminant regression classification (LCDRC) to form feature based classification method. Entropy based genetic algorithm is applied to find maximum significance between the input and output and also it radically reduces the redundancy within the input features. Experiments' using Star-Plus dataset to classify fMRI images shows that EGA-LCDRC reduces up to 60% features and produces 96.73% accuracy.

关键词： Entropy based genetic algorithm linear collaborative discriminant regression classification Functional Magnetic Resonance imaging

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