Although face verification systems have proven to be reliable in ideal environments, they can be very sensitive to real environmental conditions. The system robustness can be increased by the fusion of different face verification algorithms. To the best of our knowledge, no face verification system tried exploiting the fusion of LDA and PCA. In our opinion, the apparent strong correlation of LD...

Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into 1D vector, however recently twodimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images wit...

Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering ...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Linear Discriminant Analysis (LDA) is a well-known method for feature extraction and dimension reduction. It has been used widely in many applications such as face recognition. Recently, a novel LDA algorithm based on QR Decomposition, namely LDA/QR, has been proposed, which is competitive in terms of classification accuracy with other LDA algorithms, but it has much lower costs in time and spa...

Multiple classifier systems provide an effective way to improve pattern recognition performance. In this paper, we use multiple classifier combination to improve LDA for high dimensional data classification. When dealing with the high dimensional data, LDA often suffers from the small sample size problem and the constructed classifier is biased and unstable. Although some approaches, such as PC...

It has been demonstrated that the Linear Discriminant Analysis (LDA) approach outperforms the Principal Component Analysis (PCA) approach in face recognition tasks. Due to the high dimensionality of a image space, many LDA based approaches, however, first use the PCA to project an image into a lower dimensional space or so-called face space, and then perform the LDA to maximize the discriminato...

A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the m...