Face Recognition Based on Principle Component Analysis and Support Vector Machine

Chen, YangQuan; Lan, Libin; Zhang, Yuwei; Gu, Minjie

doi:10.1109/isa.2011.5873309

Cited by 30 publications

(10 citation statements)

References 6 publications

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“…Various state of art on facial expression recognition system was made by Bettadapura in [14]. One of the most popular and old subspace methods such as principal component analysis (PCA) [15][16][17][18][19][20] has been used in this work for projection of Fisher linear discrimiant subspace [38][39][40][41][42]. Struc and Pavesic [21] worked on Gabor filter based feature extraction by considering magnitude and phase parts separately for face recognition application.…”

Section: Related Workmentioning

confidence: 99%

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Hegde¹,

Seetha²

2017

IJIGSP

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Abstract-This paper demonstrates mainly on enhancement of extracted feature and proposes a novel approach for feature level fusion for efficient expression recognition. Extracted Gabor filter magnitude feature vector has been fused with upper face part geometrical features and Gabor phase feature vector has been fused with lower face part geometrical features respectively. Both these high dimensional feature dataset have been projected into low dimensional subspace for decorrelating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset yields higher accuracy rates compared to state of art approaches.

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Section: Related Workmentioning

confidence: 99%

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Hegde¹,

Seetha²

2017

IJIGSP

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“…Matthew Turk và các cộng sự [2] đã trình bày một hệ thống nhận dạng khuôn mặt gần như là thời gian thực bằng cách giới thiệu kỹ thuật Eigen face trong việc trích xuất đặc trưng của khuôn mặt. Chengliang Wang, Libin Lan, Yuwei Zhang and Minjie Gu [3] đã đề xuất một kỹ thuật nhận dạng khuôn mặt hiệu quả bằng cách sử dụng phương pháp phân tích thành phần chính (PCA) và máy học Support Vector Machine (SVM). Nói chung, có rất nhiều phương pháp đã được đề xuất để giải quyết bài toán nhận dạng khuôn mặt.…”

Section: Giới Thiệuunclassified

Nhận Dạng Khuôn Mặt Dựa Trên Mô Hình Xử Lý Song Song Và Phân Tán Mapreduce

Cang¹,

Son²,

Cang³

2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2017

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“…[7]. Xiaoecognition algorithm which ion using Gabor, PCA and both [7] and [8], the RBF for classification. Hence, to ace recognition, a good face should include improved fication techniques.…”

Section: Overviewmentioning

confidence: 99%

Biometric face recognition using Mexican hat wavelet kernel based SVM

Panigrahi

Biswal

Bastia

et al. 2015

2015 IEEE Power, Communication and Information Technology Conference (PCITC)

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Face recognition for biometric advantage of being a non-contact proc recognition algorithms has been proposed The face recognition system mainly consis feature extraction / reduction and classifi most popular tool, Principal Component used for feature extraction. For classificatio distance classifiers as well as Support Vecto with various kernels are used. Radial bas kernel in SVM is one of the widely use purpose. In this paper, Mexican hat wa SVM is proposed for classification and the kernel with other classification methods a proposed kernel performs better in terms vectors compared to RBF kernel and the also high with less number of features.

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Face Recognition Based on Principle Component Analysis and Support Vector Machine

Cited by 30 publications

References 6 publications

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion

Nhận Dạng Khuôn Mặt Dựa Trên Mô Hình Xử Lý Song Song Và Phân Tán Mapreduce

Biometric face recognition using Mexican hat wavelet kernel based SVM

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