Performance Analysis of Hierarchical Face Recognition

Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Neural Networks provide significant benefits in face recognition. They are actively being used for such advantages as locating previously undetected patterns, controlling devices based on feedback, and detecting characteristics in face recognition. It improves the level of accuracy compared with existing face recognition methods. The International Journal Of Science & Technoledge (ISSN 2321 – 919X) www.theijst.com 300 Vol 2 Issue 4 April, 2014 database. The experiment results show that the algorithm reduces the dimension of face feature and finds a best subspace for the classification of human face. And by optimizing the architecture of dynamic fuzzy neural network reduces the classification error and raises the correct recognition rate. So the algorithm works well on face database with different expression, pose and illumination. In this paper proposed that a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the lowfrequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher’s linear discriminated (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol. Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events, such as facial expressions, in which local information and its spatial locations should also be taken into account. In experiments with two dynamic texture databases, DynTex and MIT, both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the CohnKanade facial expression database with excellent results. Advantages of our approach include local processing, robustness to monotonic grayscale changes and simple computation In this paper proposed that a novel face representation and recognition method based on local Gabor textons. Textons, defined as a vocabulary of local characteristic features, are a good description of the perceptually distinguishable micro-structures on objects. In this paper, we incorporate the advantages of Gabor feature and textons strategy together to form Gabor textons. And for the specificity of face images, we propose local Gabor textons (LGT) to portray faces more precisely and anciently. The local Gabor textons histogram sequence is then utilized for face representation and a weighted histogram sequence matching mechanism is introduced for face recognition. Preliminary experiments on FERET database show promising results of the proposed method. In this paper proposed that a novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the no overlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects:  HGPP can describe the general face images robustly without the training procedure;  HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the sub regions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate. In this paper proposed that a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. In this paper proposed that Face recognition system based on the only single classifier considering the restricted information cannot guarantee the generality and superiority of performances in a real situation. To challenge such problems, we propose the hybrid Fourier features extracted from different frequency bands and multiple face models. The hybrid Fourier feature comprises three different Fourier domains; merged real and imaginary components, Fourier spectrum and phase angle. When deriving Fourier features from three Fourier domains, we define three different frequency bandwidths, so that additional complementary features can be obtained. After this, they are individually classified by Linear Discriminated Analysis. This approach makes possible analyzing a face image from the various viewpoints to recognize identities. Moreover, we propose multiple face models based on different eye positions with a same image size, and it contributes to increasing the performance of the proposed system. They evaluated this proposed system using the Face Recognition Grand Challenge (FRGC) experimental protocols known as the largest data sets available. The International Journal Of Science & Technoledge (ISSN 2321 – 919X) www.theijst.com 301 Vol 2 Issue 4 April, 2014 Experimental results on FRGC version 2.0 data sets has proven that the proposed method shows better verification rates than the baseline of FRGC on 2D frontal face images under various situations such as illumination changes, expression changes, and time elapses. In this paper proposed that an appearance based face recognition method called the Laplacian face approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminated Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacian faces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA and LPP can be obtained from different graph models. We compare the proposed Laplacian face approach with Eigen face and Fisher face methods on three different face datasets. Experimental results suggest that the proposed Laplacian face approach provides a better representation and achieves lower error rates in face recognition. In this paper proposed that For researchers in face recognition area have been representing and recognizing faces based on subspace discriminated analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. Novel non-statistics based face representation approach, Local Gabor Binary Pattern Histogram Sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. In this approach, a face image is modeled as a “histogram sequence” by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSes and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET face database show the validity of the proposed approach especially for partially occluded face images, and more impressively, we have achieved the best result on FERET face database. In this paper proposed that recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. They consider the case where data is drawn from sampling a probability distribution that has support on or near a sub manifold of Euclidean space. Novel subspace learning algorithm called Neighborhood Preserving Embedding (NPE). Different from Principal Component Analysis (PCA) which aims at preserving the global Euclidean structure, NPE aims at preserving the local neighborhood structure on the data manifold. Therefore, NPE is less sensitive to outliers than PCA. Also, comparing to the recently proposed manifold learning algorithms such as Isomap and Locally Linear Embedding, NPE is defined everywhere, rather than only on the training data points. Furthermore, NPE may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped. This gives rise to kernel NPE. 3. Proposed Method Module Description Four basic frame works are proposed. They are  Gabor filtering or Transformation,  Feature Extraction,  Neural Network Training and Classification Figure 1: Flowchart representation 4. Gabor Filtering or Transformation Gabor filters, which exhibit desirable characteristics of spatial locality and orientation selectively and are optimally localized in the space and frequency domains, have been extensively and successfully used in face recognition. The Gabor kernels used are defined as follows: