Face recognition by classification using radial basis function

被引：0

作者：

Mandhala, Venkata Naresh ^{[1
]}

Bhattacharyya, Debnath ^{[2
]}

Kim, Tai-Hoon ^{[3
]}

机构：

[1] Dept. of Information Technology, VFSTR University, Vadlamudi, Guntur

[2] Dept. of Computer Science and Engineering, VFSTR University, Vadlamudi

[3] Dept. of Convergence Security, Sungshin Women's University, 249-1, Dongseon-dong 3-ga, Seoul

来源：

International Journal of Multimedia and Ubiquitous Engineering | 2015年 / 10卷 / 05期

关键词：

Face recognition; Fusion; LDA; Neural networks; PCA;

D O I：

10.14257/ijmue.2015.10.5.05

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. PCA and LDA are used for the feature extraction and the resultant feature vectors are fused with the different fusion techniques and the proposed method yields good recognition rate with PCA Fusion of PCA and LDA features and those are classified with neural network. In general the size of the face database is too high and it needs more memory and needs more time for training so that to improve time and space complexities there is a need for dimensionality reduction. The extracted features are classified with Neural Network to improve the recognition rate. © 2015 SERSC.

引用

页码：33 / 40

页数：7

共 9 条

[1]

Agrawal M., Jain H.N., Kumar M., Face Recognition using Principle Component Analysis, Eigenface and Neural Network, International Conference on Signal Acquisition and Processing, pp. 310-314, (2010)

[2]

Diago L., Kitaoka T., Hagiwara I., Kambayashi T., Neuro-Fuzzy Quantification of Personal Perceptions of Facial Images Based on a Limited Data Set, IEEE Transactions on Neural Networks, 22, 12, pp. 2422-2434, (2011)

[3]

Kobayashi H., Zhao Q., Face Detection with Clustering, LDA and NN, IEEE International Conference on Systems, Man and Cybernetics, pp. 1670-1675, (2007)

[4]

Boehnen C., Barstow D., Patlolla D., Mann C., , “A multi-sample standoff multimodal biometric system”, IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 127-134, (2012)

[5]

Moon H.-M., Pan S.B., The LDA-Based Face Recognition at a Distance Using Multiple Distance Image, Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS),, pp. 249-255, (2013)

[6]

Kshirsagar V.P., Baviskar M.R., Gaikwad M.E., Face recognition using Eigenfaces, , 3Rd International Conference on Computer Research and Development (ICCRD),, 2, 3, pp. 302-306, (2011)

[7]

Sahoolizadeh A.H., Zargham Heidari B., Hamid Dehghani C., A New Face Recognition Method using PCA, LDA and Neural Network, International Journal of Computer Science & Engineering, 2, 4, (2008)

[8]

Sharif M., Mohsin S., Jamal M.J., Raza M., , “Illumination Normalization Preprocessing for Face Recognition”, International Conference on Environmental Science and Information Application Technology (ESIAT), 2, pp. 44-47, (2010)

[9]

Venkatrama Phani Kumar S., Kishore K.V., Kumar K.H., , “Hybrid Face Recognition with Locally Discriminating Projection”, International Conference on Signal Acquisition and Processing, pp. 327-331

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