PCA and LDA Based Face Recognition Using Feedforward Neural Network Classifier

Eleyan, Alaa; Demirel, Hasan

doi:10.1007/11848035_28

Cited by 44 publications

(24 citation statements)

References 14 publications

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“…The performance of PCA and LDA based facial recognition systems in the same setup [6] is lower than the proposed method. Table 3 shows the results of the comparison between the second experiment and the work [6].…”

Section: Comparing To Other Workmentioning

confidence: 77%

“…Table 3 shows the results of the comparison between the second experiment and the work [6]. The results of the comparison are depicted in Figure 9.…”

Section: Comparing To Other Workmentioning

confidence: 99%

“…Facial recognition solutions can be divided to two categories, such as, statistics-based and structure-based methods. For statistics-based method [1,3,5,6], the face is treated as a two dimensional pattern of intensity variation. Under this method, the face is matched through finding its underlying statistical regularities.…”

Section: Introductionmentioning

confidence: 99%

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Facial Recognition based on Histogram Matching with Adaptive Threshold

Anh¹

2017

CAE

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Facial recognition was a field that was extensively studied in the past years but it is still an active area of research. This paper proposes a new method by matching histogram with adaptive threshold. The proposed method is simple but effective and it can be use for real-time system. Publicly available AT&T database is used for the evaluation of the proposed method, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. The proposed method provides a recognition rate higher than 99% and a verification error lower than 1%.

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Section: Comparing To Other Workmentioning

confidence: 77%

“…Table 3 shows the results of the comparison between the second experiment and the work [6]. The results of the comparison are depicted in Figure 9.…”

Section: Comparing To Other Workmentioning

confidence: 99%

See 1 more Smart Citation

Facial Recognition based on Histogram Matching with Adaptive Threshold

Anh¹

2017

CAE

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“…In a set of the N d-dimensional image's for the learning set (d being to all the number of the pixels in the each and in every image), the PCA method extracting that are known as principal components as to be the eigenvectors of all the covariance matrix of the overall data. By the perceptive, that we mean's that the features that can be used by all the various human being for the discriminate between or to the describer's of the faces [8]. It is the mathematical procedure that can performs all the dimensionality reduction by all the extracting the principal components of all the multi-dimensional data.…”

Section: Techniques For Feature Extractionmentioning

confidence: 99%

A Hybrid Feature Extraction Technique for Face Recognition

Sharma¹,

Chhabra²

2017

IJARCSSE

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Abstract-Pervasive work out is the expertise that is elegantly integrated in our everyday life. Form of home automation focuses on making it possible for elderly and for alone person to remain at home, safe and comfortable. The care of elderly could be enhanced through remote monitoring system, sensor technologies and communication systems. A form of home automation focuses on making it possible for elderly and individual people to remain at home safe and comfortable. Face Recognition is a concept under pattern recognition with applications moving towards the use of facial features for authorization and authentication. Hybrid approach has a special status among face recognition systems as they combine different feature extraction approaches to overcome the shortcomings of individual methods. This work uses Feed Forward Neural Network for face recognition and using a combination of PCA and ICA algorithm for feature extraction. Proposed algorithm is implemented in MATLAB and performance evaluation of success rate of proposed algorithm with existing techniques.Results show that proposed system has higher accuracy than face recognition using single feature extractor.

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“…As the set of all Laurent polynomials exhibits a commutative ring structure, within which polynomial division with remainder is possible, long division between Laurent polynomials is not a unique operation [12]. The Euclidean algorithm [12] can be used to decompose P(z) ܲ ෨ ሺܼሻ as…”

Section: Lifting Wavelets Transformmentioning

confidence: 99%