Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

Live facial feature extraction

Precise facial feature extraction is essential to the high-level face recognition and expression analysis. This paper presents a novel method for the real-time geometric facial feature extraction from live video. In this paper, the input image is viewed as a weighted graph. The segmentation of the pixels corresponding to the edges of facial components of the mouth, eyes, brows, and nose is implemented by means of random walks on the weighted graph. The graph has an 8-connected lattice structure and the weight value associated with each edge reflects the likelihood that a random walker will cross that edge. The random walks simulate an anisot- ropic diffusion process that filters out the noise while preserving the facial expression pixels. The seeds for the segmentation are obtained from a color and motion detector. The segmented facial pixels are represented with linked lists in the origi- nal geometric form and grouped into different parts corresponding to facial components. For the convenience of implementing high-level vision, the geometric description of facial component pixels is further decomposed into shape and reg- istration information. Shape is defined as the geometric information that is invariant under the registration transformation, such as translation, rotation, and isotropic scale. Statistical shape analysis is carried out to capture global facial fea- tures where the Procrustes shape distance measure is adopted. A Bayesian ap- proach is used to incorporate high-level prior knowledge of face structure. Experimental results show that the proposed method is capable of real-time extraction of precise geometric facial features from live video. The feature extraction is robust against the illumination changes, scale variation, head rotations, and hand interference.

Facial expression feature extraction method based on improved LBP

Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global features extracted.To solve these problems,a facial expression feature extraction method is proposed based on improved LBP.Firstly,LBP is converted into double local binary pattern(DLBP).Then by combining Taylor expansion(TE)with DLBP,DLBP-TE algorithm is obtained.Finally,the DLBP-TE algorithm combined with extreme learning machine(ELM)is applied in seven kinds of ficial expression images and the corresponding experiments are carried out in Japanese adult female facial expression(JAFFE)database.The results show that the proposed method can significantly improve facial expression recognition rate.

FACE RECOGNITION BASED ON WAVELET-CURVELET-FRACTAL TECHNIQUE

In this paper,a novel face recognition method,named as wavelet-curvelet-fractal technique,is proposed. Based on the similarities embedded in the images,we propose to utilize the wave-let-curvelet-fractal technique to extract facial features. Thus we have the wavelet’s details in diagonal,vertical,and horizontal directions,and the eight curvelet details at different angles. Then we adopt the Euclidean minimum distance classifier to recognize different faces. Extensive comparison tests on dif-ferent data sets are carried out,and higher recognition rate is obtained by the proposed technique.