Face recognition using DWT and eigenvectors

Rao, M Koteswara; Swamy, K. Veera; Sheela, K. Anitha

doi:10.1109/et2ecn.2012.6470102

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…Feature selection is one of the important points. It has been found that appearance based methods are more successful [4]. ill this, the operations are performed on 2-D images directly.…”

Section: Introductionmentioning

confidence: 99%

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“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

Gupta¹,

Malage²,

More³

et al. 2014

2014 International Conference on Advances in Engineering &Amp; Technology Research (ICAETR - 2014)

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Due to usefitlness in recognition and identification biometric systems have become a major part of research. Paper proposes a multimodal biometric system using face modality combined with palm print and palm vein modality. The proposed methodology uses Local Statistical method in which pre-defined block of DCT coefficient were used to calculate standard deviation and store them as feature vector. Matching is done using distance between foature vector of testing and training data set. Results show that the Genuine Acceptance Rate (GAR) of foature level fusion is J 00% which is better than, that of uni-modal systems, hence having multimodality is advantageous. For testing and training database of J 00 students of College of Engineering Pune.

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“…Feature selection is one of the important points. It has been found that appearance based methods are more successful [4]. ill this, the operations are performed on 2-D images directly.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, for databases with dynamic nature, complexity increases due to repetition of eigen related calculations. LFA and EGBM [4] technique belongs to hybrid method. In these methods the colour, shape, descriptors are used for face recognition.…”

Section: Introductionmentioning

confidence: 99%

“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

Gupta¹,

Malage²,

More³

et al. 2014

2014 International Conference on Advances in Engineering &Amp; Technology Research (ICAETR - 2014)

View full text Add to dashboard Cite

show abstract

“…In [8], DWT and two dimensional PCA is explored with 92% classification accuracy. In [9], the finer detail subband is extracted using DWT and PCA is applied on it. In [10], low frequency subband is extracted using DWT and is divided into subimages.…”

Section: Introductionmentioning

confidence: 99%

An improved feature extraction method based on DWT and 2DSubXPCA methods

Devi¹,

Rao²

2015

2015 International Conference on Computing and Network Communications (CoCoNet)

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Face is one of the popular biometric used in human authentication for providing secured access. Feature reduction is a challenging task for fast and efficient recognition. Many techniques like Discrete wavelet transform (DWT), Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been used for feature reduction. Some issues like pose variation, change in facial expression and poor illumination make the recognition process a difficult task. In this paper, we bring in a hybrid approach based on DWT and 2DSubXPCA for feature extraction and use nearest neighbor for classification. A maximum classification accuracy of 97.5% with highest dimensionality reduction is obtained for ORL database.

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“…In 2012, Rao [7] proposed a facial recognition system using discrete wavelet transform (DWT) and eigenvectors, showing an average of 3.25% improvement in recognition performance.…”

Section: Related Workmentioning

confidence: 99%

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

Santoso¹,

Harjoko²,

Putra³

2017

ijacsa

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Abstract-The main weakness of the k-Nearest Neighbor algorithm in face recognition is calculating the distance and sort all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved by utilizing the priority k-d tree search to speed up the process of k-NN classification. This paper proposes a method for student attendance systems in the classroom using facial recognition techniques by combining three levels of Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying the priority of k-d tree search to speed up the process of facial classification using kNearest Neighbor. The proposed algorithm is tested on two datasets that are Honda/UCSD video dataset and our dataset (AtmafaceDB dataset). This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor on our dataset is 95.56% with k = 5, whereas in the Honda / UCSD dataset it is only 82% with k = 3. The proposed method gives computational recognition time on our dataset 40 milliseconds.

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Face recognition using DWT and eigenvectors

Cited by 10 publications

References 17 publications

“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

An improved feature extraction method based on DWT and 2DSubXPCA methods

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

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Face recognition using DWT and eigenvectors

Cited by 10 publications

References 17 publications

&#x201C;Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform&#x201D;

&#x201C;Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform&#x201D;

An improved feature extraction method based on DWT and 2DSubXPCA methods

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

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“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”