Ning Zheng

Generalized multiple maximum scatter difference feature extraction using QR decomposition

Multiple maximum scatter difference (MMSD) discriminant criterion is an effective feature extraction method that computes the discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. However, singular value decomposition (SVD) of two times is involved in MMSD, rendering this method impractical for high dimensional data. In this paper, we propose a generalized MMSD (GMMSD) criterion for feature extraction and classification. GMMSD allows relatively-free selection of a suitable transformation matrix to reduce dimensions. Based on GMMSD criterion, we demonstrate that the same discriminant information can be extracted by QR decomposition, which is more efficient than SVD. Next, GMMSD is compared with several classical feature extraction methods to justify the validity of the proposed method. Our experiments on three face databases and two facial expression databases demonstrate that GMMSD provides favorable recognition performance with high computational efficiency.

Generalized MMSD feature extraction using QR decomposition

Multiple Maximum scatter difference (MMSD) discriminant criterion is an effective feature extraction method that computes the discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. However, singular value decomposition (SVD) of two times is involved in MMSD, making this method impractical for high dimensional data. In this paper, we propose a novel method for feature extraction and classification based on MMSD criterion, called generalized MMSD (GMMSD), which employs QR decomposition rather than SVD. Unlike MMSD, GMMSD does not require the computation of the whole scatter matrix. Instead, it computes the discriminant vectors from both the range of whitenizated input data matrix and the null space of the within-class scatter matrix. We evaluate the effectiveness of the GMMSD method in terms of classification accuracy in the reduced dimensional space. Our experiments on two facial expression databases demonstrate that the GMMSD method provides favorable performance in terms of both recognition accuracy and computational efficiency.

Parameters estimation of the LFM signal based on the optimum seeking method and fractional Fourier transform

A new parameter estimation of LFM signal is presented. First it is assumed that delay multiplication and the Fourier transform can be employed to get a rough estimation of the frequency rate of the LFM signal, for determining a approximate range of the LFM signals rotation angle in the fractional Fourier domain, and then the exact frequency rate and initial frequency are obtained through the optimum seeking method and fractional Fourier transform. Simulation experiments show that this method greatly reduces the computational complexity on the premise of accuracy.

Multiple-manifolds Discriminant Analysis for Facial Expression Recognition from Local Patches Set

In this paper, a novel framework is proposed for feature extraction and classification of facial expression recognition, namely multiple manifold discriminant analysis (MMDA), which assumes samples of different expressions reside on different manifolds, thereby learning multiple projection matrices from training set. In particular, MMDA first incorporates five local patches, including the regions of left and right eyes, mouth and left and right cheeks from each training sample to form a new training set, and then learns projection matrix from each expression so that maximizes the manifold margins among different expressions and minimizes the manifold distances of the same expression. A key feature of MMDA is that it can extract the discriminative information of expression-specific for classification rather than that of subject-specific, leading to a robust performance in practical applications. Our experiments on Cohn-Kanade and JAFFE databases demonstrate that MMDA can effectively enhance the discriminant power of the extracted expression features.

Application of the 2D-FrFT combined with fuzzy fusion classification algorithm in human emotional state recognition

In this paper we perform feature extraction by utilizing the Two Dimensions Fractional Fourier Transform(2D-FrFT). PCA is used to reduce the high-dimensional feature information and we perform FLDA on the total samples, then emotional sates are recognized based on multi-classifier fusion with fuzzy integral in decision layer. Simulations based on the Ryerson and Jaffe facial expression database show that the novel method is invalid and superior. Compared to the classical algorithm, the proposed method may flexibly extract the emotional features and effectively classify the different emotional states, so highly improve the recognition rate.

Navigation Satellite Passive Radar Moving Target Detection and SAR Imaging Based on FRFT

Conventional radars have less invisibility, but the passive radars based on TV, FM signals also have some restrictions in SAR imaging by a range of issues such as ground electromagnetic environment and signal silence. This paper presents a new method of passive SAR imaging based on navigation satellite signal with single/multiple receive-only mode in FRFT domain. The received reflection of single multiple GPS signals from target is approximately linear FM signals which can be transformed by FRFT for signal frequency and Doppler frequency detection and estimation, especially for the target SAR imaging. Simulation results shown the effectiveness of the algorithm, and help to avoid the multi-target SAR imaging interference.

Incremental generalized multiple maximum scatter difference with applications to feature extraction

Abstract In this paper, we propose a new algorithm to implement the generalized multiple maximum scatter difference (GMMSD). Due to enhanced features of this algorithm over the original GMMSD, we named it GMMSD+. By employing a different projection from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix, GMMSD+ can divide the centroid vector of each class into two components: intrinsic common component (ICC) and discriminant difference component (DCC), and then automatically discards ICC which contains little discriminative information, while keeping DCC which contains the true discriminative power. Next, we introduce a practical implementation of GMMSD+, which can accurately and efficiently update the discriminant vectors with new training samples incrementally, eliminating the complete re-computation of the training process. Our experiments demonstrate that incremental version of GMMSD+(IGMMSD+) eliminates the complete re-computation of the training process when new training samples are presented, leading to significantly reduced computational cost.

Two-dimensional discriminant multi-manifolds locality preserving projection for facial expression recognition

In this paper, we assume that samples of different expressions reside on different manifolds and propose a novel human emotion recognition framework named two-dimensional discriminant multi-manifolds locality preserving projection (2D-DMLPP). 2D-DMLPP focuses on salient regions which reflect the significant variation from facial expression images so that it can learn an expression-specific model from salient patches rather than that of subject-specific. Furthermore, conventional manifold learning methods ignore the variation among nearby samples from the same class, leading to serious overfitting. We construct three adjacency graphs to model the margin and information, including diversity and similarity of salient patches from the same expression, and then incorporate the information and margin into dimensionality reduction function. Several experiments show that the proposed method significantly improves the recognition performance of facial expression recognition.

Incremental GMMSD2 with applications to feature extraction

The generalized MMSD (GMMSD) is considered an efficient implementation of MMSD to extract discriminative information. However, a significant issue with the implementation of GMMSD is the complete recomputation of the training process when new training samples are presented. In this paper, we propose an alternative solution for feature extraction using the principles of GMMSD, which we call GMMSD2. GMMSD2 only requires the computation of centroid matrix, and it can overcome computational cost by applying efficient QR-updating techniques when new training samples are presented. Our experiments on FERET database demonstrate that incremental version of GMMSD2 eliminates the complete recomputation of the training process when new training samples are available, leading to significantly reduced computational cost.

Recognizing facial expression based on discriminative multi-order Two Dimensions Fractional Fourier Transform

Two Dimensions Fractional Fourier Transform (2D-FrFT) is a powerful tool for feature extraction in facial expression recognition. However, a single order 2D-FrFT feature is not effective enough. In this paper, it proposes a facial expression recognition system based on multi-order with 2D-FrFT features. Moreover, a multi-feature fusion algorithm which is named Discriminative Multi-set Canonical Correlation Analysis (DMCCA) is deduced. Fusion of multi-order 2D-FrFT features by DMCCA can significantly improve the recognition performance, due to increased amount of discriminative information. More important, an objective function is defined to find the optimal 2D-FrFT orders group fused by DMCCA. Experiment results demonstrate the effectiveness of the method.