Yanjiang Wang

Facial expression recognition based on Gabor features and sparse representation

In this paper, we present a facial expression recognition method based on Gabor feature and sparse representation. Sparse Representation based Classification (SRC) has been widely used in computer vision and pattern recognition. And Gabor filter banks can be used to approximately model the signal processing in visual primary cortex. We believe that the nature of the attractive performance of SRC and Gabor feature lies in that they both followed the natures of signal perception of retina and information processing of cortex in human vision. Therefore, we combined the Gabor feature and SRC for facial expression recognition. The comparison experiments of proposed Gabor+SRC algorithm and straightforward SRC application are conducted on JAFFE database. And the experimental results showed the attractive performance of the proposed Gabor+SRC method.

An effective eye states detection method based on projection

An effective multiple eye-state detection method based on projection is proposed. The structure of an eye sketch map is analyzed. Using integral projection method, the height and width of eye iris could be evaluated. And then the ratio between the height and width of the eye iris visible is selected as the criterion to determine the eye states. The experimental results showed the efficiency of eye states detection method proposed.

Facial expression recognition based on Hessian regularized support vector machine

Semi-supervised learning (SSL) has achieved attractive performance in many pattern recognition areas including image annotation, object recognition, face recognition and facial expression recognition. The state of the art SSL algorithm is Laplacian regularization (LR) which determined the underlying manifold using graph Laplacian. However, LR suffers from the lack of extrapolating power which will be towards the constant function for the data points beyond the boundary of domain. In contrast to LR, Hessian regularization (HR) can well steer the function varying smoothly along the manifold. In this paper, we present Hessian regularized support vector machine (SVM) for facial expression recognition (FER). We carefully conduct experiments on JAFFE dataset. The experimental results show that HR based SVM (HesSVM) outperforms SVM and LR base SVM (LapSVM).

Detection of static salient objects based on visual attention and edge features

Object detection algorithm based on the traditional saliency map often has problems of unable to locate and extract static salient objects precisely, besides, the position located does not coincide with the actual size of the object. In order to solve these problems, an improved algorithm of static salient objects detection is proposed in this paper, which combines visual attention mechanism with the edge information. First, coarse detection is realized according to an improved saliency map, then fine detection is implemented through edge detection by wavelet transform. Finally, mathematical logic operations are performed on the above two saliency maps, thus making the location and extraction of the static salient objects more accurately than traditional methods. Experimental results demonstrate the efficacy of the proposed method.

Memory-based Gaussian Mixture Modeling for moving object detection in indoor scene with sudden partial changes

In this paper, a memory-based Gaussian Mixture Model (MGMM) is proposed inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background, which can make the model remember what the scene has ever been and help the model adapt to the variation of the scene more quickly. Experimental results show the effect of the memory mechanism in segmenting moving objects with sudden partial changes in the background scene.

Sparse eigenfaces analysis for recognition

Face recognition plays a fairly vital role in human computer inter-action. Eigenface algorithm is one representative approach that projects face images onto a low dimensional feature space using principal components analysis (PCA) to choose the maximal total scatter across all classes. However, due to the linear combination of original samples, eigenfaces may be difficult to interpret and explain. In this paper, we present sparse eigenfaces method for face recognition which employs sparse principal components analysis (SPCA) to find sparse eigenfaces capturing the maximal variance of all face images. Particularly, sparse eigenfaces can achieve reasonable trade-off between interpretability and the maximal variance projection of all face images. Finally, we carefully conduct face recognition experiments on Yale face data set. The experimental results demonstrate that sparse eigenfaces method outperforms the traditional eigenfaces method.

Subject-Independent Facial Expression Recognition with Biologically Inspired Features

Despite of much research for facial expression recognition, recognizing facial expressions across different persons is still a challenging computer vision task. However, facial expression analysis seems naturally for human visual system. Motivated by visual biology, this paper proposes an invariant feature extraction method for subject-independent facial expression recognition. In particular, we extract the biologically inspired facial features using extended visual cortex model-HMAX which consist of a template matching and a maximum pooling operation. We carefully organized the facial features and achieve subject-independent facial expression recognition using a sparse representation based classifier. The experiments on Yale database and JAFFE database demonstrate the significance of our proposed method for subject-independent facial expression recognition.

Spinning tri-layer-circle memory modeling for template updating during moving object tracking

Inspired by the mechanism of human brain three-stage memory model, this paper develops a spinning tri-layer-circle memory model(STLC-MM) and applies it for template updating during object tracking. Three memory spaces are defined to store and process the object templates used in the tracking framework. Each memory space, which has a fixed input window and a fixed output window, is denoted by a circle and can spin with different speed. With three circle memory spaces spinning, templates in the three memory spaces are updated by imitating the cognitive process of memorization, recall, and forgetting. Then all the templates in the output windows of the three memory spaces are compared with the estimated template respectively, and the most similar template is selected as the final output of the STLC-MM. Finally, STLC-MM is incorporated into a particle filter (PF) framework in order to verify the effect of our proposed model. Experimental results show that the proposed method is more robust to sudden appearance changes and serious occlusions.

Moving object detection based on HFT and dynamic fusion

To solve the problem of detecting salient moving object in the video shot by static camera, a new spatio-temporal object detection algorithm is proposed in this paper. Firstly, Hypercomplex Fourier Transform (HFT) is used to the current video frame to achieve the static salient region; then, the moving salient region is detected by an improved three frames difference algorithm; finally, the static salient region and the moving salient region are combined with dynamic fusion strategy. Compared with the traditional techniques, the proposed method is in better correspondence with the response of the human visual system and more suitable for salient moving object detection. The experimental results demonstrate the effectiveness of the proposed object detection method.

Bio-inspired invariant visual feature representation based on K-SVD and SURF algorithms

In this paper, a bio-inspired invariant visual feature representation method is proposed. A set of Gabor filters with different parameters and global max operation are performed to improve the adaptability to scale and shift changes. In order to extract rotation-invariant features of images, the K-SVD and SURF algorithms are introduced into the traditional HMAX model. Prototypes (feature templates) are learned by the K-SVD algorithm, while the SURF descriptor of patches aims to enhance the rotation invariance. Experimental results on image classification demonstrate the superiority of the proposed feature representation method.