Double Sides 2DPCA for Face Recognition

Lu, Chong; Liu, Wanquan; Liu, Xiaodong; An, Senjian

doi:10.1007/978-3-540-87442-3_56

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Face recognition is a significant gift that humans perform effortlessly and automatically in our daily lives. According to the wide applications of automatic face recognition, such as public security and human-robot interaction, it is an established field attracting great attention [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

Han

Zhao

2019

Journal of Electrical and Computer Engineering

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In the actual face recognition applications, the sample sets are updated constantly. However, most of the face recognition models with learning strategy do not consider this fact and using a fixed training set to learn the face recognition models for once. Besides that, the testing samples are discarded after the testing process is completed. Namely, the training and testing processes are separated and the later does not give a feedback to the former for better recognition results. To attenuate these problems, this paper proposed an online sparse learning method for face recognition. It can update the salience evaluation vector in real time to construct a dynamical facial feature description model. Also, a strategy for updating the gallery set is proposed in this proposed method. Both the dynamical facial feature description model and the gallery set are employed to recognize faces. Experimental results show that the proposed method improves the face recognition accuracy, comparing with the classical learning models and other state-of-the-art face recognition methods.

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Section: Introductionmentioning

confidence: 99%

More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

Han

Zhao

2019

Journal of Electrical and Computer Engineering

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“…2DPCA [7] algorithm directly works on two-dimensional matrix, which can easily find the feature vector and the feature matrix, greatly reduce the dimensions of the image, improve the recognition rate and computational efficiency. 2DPCA is better than PCA algorithm when applied to face recognition, but it's performance is still affect by the environment.…”

Section: Introductionmentioning

confidence: 99%

Near infrared face recognition based on wavelet transform and 2DPCA

Feng

Liu

et al. 2010

2010 International Conference on Intelligent Computing and Integrated Systems

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A feature extraction algorithm of near-infrared human face based on wavelet transform and 2DPCA (two dimension principal component analysis) was proposed. This algorithm firstly applied wavelet transform to the near infrared face images, obtained the low frequency components of face images, and removed high frequency components. Secondly applied 2DPCA to the low frequency components of the face images for feature extraction. Finally completed the face recognition using Euclidean distance .The experimental results based on near-infrared face database clearly showed that the proposed algorithm could get higher recognition rate than the traditional PCA and 2DPCA algorithm, which demonstrated the efficiency of the proposed method.

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“…Although there are many PCA algorithm to treat with online learning, two dimensional incremental PCA is still scarcity. Lu et al [8] gave an incremental 2dpca for face recognition, which can implement online learning.…”

Section: Introductionmentioning

confidence: 99%

Efficient two dimensional principal component analysis for online learning

Shi

2009

2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA)

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Recently, two dimensional Principal Component Component (2dPCA) has attracted much attention, which need not to transform the matrix into vector like traditional PCA. But it has some disadvantage when faced with large-scale data set or online learning, the covariance matrix must be recom puted. In addition, the storage problem also makes its computation im possible. In this paper, an efficient online learning algorithm is proposed to treat with the problem. The essence of two dimensional PCA was firstly analyzed, which states that the column vector or row vector for each image can be treated as the special input vector. Thus, it can be used as input sample for the general incremental PCA algorithm. The proposed method uses less storage and has quick convergence. The effectiveness is demonstrated by the experiment results on the ORL and YaieB databases.

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Double Sides 2DPCA for Face Recognition

Cited by 7 publications

References 11 publications

More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition

Near infrared face recognition based on wavelet transform and 2DPCA

Efficient two dimensional principal component analysis for online learning

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