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The key problem of colour face recognition technique is how to take full advantage of the colour information and extract effective discriminating features. To solve this problem, the authors propose a novel non-linear feature extraction approach for colour face recognition, named dual multi-kernel discriminating correlation analysis, which separately maps different colour components of face images...
Face diagnosis of Traditional Chinese Medicine (TCM) is carried out by observing the facial complexion to obtain the disease diagnostic results. Color space based on human visual system will be more conducive to facial complexion recognition, which is more suitable to measure and distinguish facial complexion. Uniform color space based facial complexion recognition for TCM is proposed in this paper,...
To overcome the shortcomings of the traditional methods, this paper proposes a novel face recognition method based on the image latent semantic features and ensemble extreme learning machine. The image latent semantic analysis is to acquire the high-level features from the face image, which has good robustness to illumination and expression changes. The image latent features are extracted as fellows:...
In face recognition, LBP (Local Binary Patterns) is a very popular method, which can solve the defects of the traditional local feature extraction methods with fixed scale and small extraction scale. However, the LBP operator only describes the relationship between the center pixel and its neighborhood pixels, it ignores the relationship among the operators. 3DLBP (3 Dimensions Local Binary Patterns)...
This paper addresses the problem of tracking and recognizing faces via incremental local sparse representation. We first develop a robust face tracking algorithm based on the local sparse appearance. This sparse representation model exploits both partial and spatial information of the face based on a covariance pooling method. Following in the face recognition stage, with the employment of a novel...
Kernel method is an effective technique in extracting nonlinear discriminative features. In this paper, we propose a new color face image recognition approach based on kernel holistic orthogonal analysis (KHOA) of discriminant transforms. Original color face images are mapped to high dimensional feature space by kernel function, then extract discriminant transforms of red, green, blue color image...
Facial expression recognition plays an important role in interactive entertainment. In this paper, LSFA (Local Sensitive Frontier Analysis) a novel feature extraction method is introduced for facial expression recognition. LSFA is designed as manifold based feature extraction method to obtain useful features from the facial expression pictures, since the facial expression scatter in high dimensional...
Facial feature tracking is a key step in facial dynamics modeling and affect analysis. Active Shape Model (ASM) has been a popular tool for detecting facial features. However, ASM has its limitations. Due to the finiteness of the training set, it cannot handle large variations in facial pose exhibited in video sequences. In addition, it requires accurate initiation. In order to address these limitations,...
Given a video sequence containing face candidates, detecting faces is a challenging problem, which can be attributed to the difficulty in handling the appearance variability of the face. Based on skin color segmentation combined with the saliency model, a novel method is proposed to detect human faces in videos. Firstly, a skin color model in the YCbCr chrominance space is built to segment skin-color...
In this paper, a novel Gabor-2DFisherface approach with selecting 2D Gabor principal components and discriminant vectors is proposed for face recognition. Gabor transform is an important frequency-domain analysis tool. The proposed approach combines it with discriminant analysis technique. This approach first preprocesses all image samples by using Gabor transform, and then calculates 2D Gabor principal...
Real-time human face detection and recognition from video sequences in surveillance applications is a challenging task due to the variances in background, facial expression and illumination. The face detection approach is based on modest AdaBoost algorithm and can achieve fast, accurate face detection that is robust to changes in illumination and background. The detection stage provides good results...
Illumination variation is one of the critical factors affecting face recognition rate. A novel illumination compensation for face recognition is presented in this paper. First of all, a new illumination direction map is introduced, which is built using the block-based histogram equalization (Xudong Xie and Kin-Man Lam, 2005) and illumination model. Secondly, plane-fit method can be used to determine...
Particle filter is widely used in object tracking. However, it has one notable weaknesses that is sample degeneracy problem. This paper proposes a novel algorithm to overcome this problem by incorporating mean shift into particle filtering. Mean shift reacting on sample herds the samples in the reference mode area, which could make less samples be used while tracking. The proposed algorithm is used...
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