A Novel Hybrid Approach Based on Sub-pattern Technique and Extended 2DPCA for Color Face Recognition

Chen, Shangyou; Sun, Yanfeng; Yin, Baocai

doi:10.1109/ism.2009.24

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The velocity of aelement is given as Vi= v1, v2…vn. Also, everyelement has a native memory (pBest) which retains the best location that is experienced by the element so far [10,11,12]. A universallymutual memory (gBest) keeps the best global position established so far.…”

Section: Feature Optimizationmentioning

confidence: 99%

An Efficient Machine Learning Method for Facial Expression Recognition

2019

IJITEE

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Emotions play important role in human sentiments so broad studies are carried out to explore the relation between human sentiments and machine interactions. This paper deals with an automatic system which spontaneously identifies the facial emotion. Gradient filtering and component analysis is used to extract feature vector and feature optimization is taken place using swarm intelligence approach. Thus emotion recognition with optimized feature extraction process is carried out with high accuracy rate and less error probabilities. Finally the testing process is obtained for the classification of emotions and then performance is measured in terms of false acceptance rate, false rejection rate, and accuracy.

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Section: Feature Optimizationmentioning

confidence: 99%

An Efficient Machine Learning Method for Facial Expression Recognition

2019

IJITEE

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“…But E2DPCA is not applicable for color images. Therefore Sub pattern Extended 2-Dimensional PCA (SpE2DPCA) is introduced for color face recognition [6]. The recognition rate is higher than PCA, 2DPCA, E2DPCA and problem of small sample size in PCA is also eliminated.…”

Section: F Sub Pattern Extended 2-dimensional Pcamentioning

confidence: 99%

“…Therefore 2-dimensional Principal Component Analysis [4] is introduced. Since these techniques are applicable only in gray scale images, Global Eigen Approach [5] and Sub pattern Extended 2-dimensional Principal Component Analysis [6](E2DPCA) can be extended by traditional approaches to color space. Multilinear Image Analysis [7] introduced tensor concept which allows more than one factor variation in contradiction to PCA.…”

Section: Introductionmentioning

confidence: 99%

A Literature review on Facial Expression Recognition Techniques

Ms.Aswathy.R¹

2013

IOSR-JCE

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Automatic facial expression recognition has become a progressive research area since it plays a major role in human-computer-interaction. The facial expression recognition finds major application in areas like social interaction and social intelligence. A review of various techniques used in facial expression recognition like principal component analysis (PCA), linear discriminant analysis (LDA) etc is done in this paper. The survey is represented in tabular form for quick reference Index Terms

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“…Where some local and color information may be ignored. Thus, Chen et al [13] provide a solution to such problems by proposing SpE2D-PCA a hybrid approach based on sub-pattern technique and E2D-PCA.…”

Section: Color Quaternion Pca (Q-pca) Approachmentioning

confidence: 99%

“…However, computing those of a quaternion matrix is a difficult task [13]. In order to calculate the PCA projection matrix based on a quaternion representation, it is necessary to obtain the orthogonal eigenvector set of the covariance.…”

Section: Quaternion Principal Component Analysis (Q-pca)mentioning

confidence: 99%

Color face recognition using quaternion PCA

Jaha

Ghouti

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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Recently, biometric systems have attracted the attention of both academic and industrial communities. Advances in hardware and software technologies have paved the way to such growing interest. Nowadays, efficient and cost-effective biometric solutions are continuously emerging. Fingerprint-based biometric systems have emerged as pioneering commercial applications of biometric systems. Face and iris traits have proven to be reliable candidates. Until recently, face recognition research literally followed the research undertaken in the field of fingerprint recognition which is inherently gray-scale. In this paper, efforts are restricted to the investigation of face representations in the color domain. The concept of principal component analysis (PCA) is carried over into the hypercomplex domain (i.e., quaternionic) to define quaternionic PCA (Q-PCA) where color faces are compactly represented. Unlike the existing approaches for handling the color information, the proposed algorithm implicitly accounts for the correlation that exists between the face color components (i.e., red, green and blue, respectively).

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A Novel Hybrid Approach Based on Sub-pattern Technique and Extended 2DPCA for Color Face Recognition

Cited by 9 publications

References 9 publications

An Efficient Machine Learning Method for Facial Expression Recognition

An Efficient Machine Learning Method for Facial Expression Recognition

A Literature review on Facial Expression Recognition Techniques

Color face recognition using quaternion PCA

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