Research On Highly Accurate Face Recognition Algoirhtms

Human has an excellent appetence on face recognition, it's our dream to makes the machine has the same intelligent recognition ability. Original dream and curiosity drive people conduct the continuing research on automatic face recognition. As the development of the modern information technologies, automatic face recognition is attached importance to broad fields such military, commercial, security, in virtue of its good applicability and non-intrusive property. Face recognition has become one of the most representative and challenging research content of pattern recognition domain. Face recognition is an old but young academic problem, about which people have though across three century. As early as 1888, Nature magazine published the first academic paper on face recognition, starting people's exploration on face recognition.120 years later, i.e.2008, Science magazine published a technical comment on "100% automatic face recognition accuracy", which pointed out the highly accurate face recognition in the real world application is still an ambitious goal and its solution require me to continue the innovative research.Face recognition is a special complex pattern recognition problem, whose particularities lie in its high feature dimensionality versus small sample size, the large within-class variations versus the small between-class variations, and so on. These particularities make the mature pattern classification theory cannot be applied to solve face recognition problem, which quality the feature extraction as the determinant of the accuracy level. Unfortunately, traditional feature extraction methods often start from a specific object function, which makes the algorithm can adapt to the specific variations contained in a certain data set, and thus achieve high recognition accuracy. However, they cannot solve the real world recognition problem in complex conditions. In order to improve state-of-the-art performance, we induce two core ideas in this paper. Firstly, capitalize on the prior knowledge on the class configuration of the face feature space, design the algorithm that can adapt to the intrinsic property of the face pattern. Secondly, conduct the take an interdisciplinary research, drawing from the accumulated and vast knowledge of both the computer vision and psychology communities to solve the face recognition problem. Inspired by the two core ideas, the major research contents of this paper are as follows.(1) Explore the class configuration of the face space by global scatter analysis, local overlap analysis, manifold structure analysis and classification error, and draw a conclusion that the each face class share a common structure in the measurement space, and their structures high overlap in the space. Based on these characteristics, we suggests two important prior assumption on the face space:First, each class share the same covariance matrix, the within-class scatter is much larger than the between-class scatter, which makes the class conditional covariance is roughly equal to the global covariance of the whole data set. Second, the difference between different persons resides in the same between-class subspace, and the principal direction of the between-class subspace is not conflict with the within-class subspace. These two prior assumptions makes us can train the algorithm in a large generic data set. The large-scale experiments on FERET database shows the traditional algorithm that makes use of these prior assumptions can largely outperform the best results reported in the international evaluation.(2) A family of linear feature extraction methods, such as PCA, ICA, LDA, LPP, and UDP, has been proved effective to address this challenging problem. In this paper we unify these methods into a projection pursuit framework with different projection indices, and suggest that their feasibility on face recognition mainly come from the whitening process. We propose a locality pursuit algorithm, which pursues the optimum projection that preserves or dissipates the local clusters in the whitened space. The experiments using AR and FERET databases show that the proposed method achieves better face recognition performance than other unsupervised methods.(3) Inspired by the geometric intuition of "sample uniform", we propose a locality pursuit algorithm, which aims to solve the challenging face recognition problem with single image per person. The idea of this algorithm is to disperse the samples which are close in the measurement space, and thus reduce the risk of recognition error. Experiments on the FERET database that contains 1196 persons show that locality pursuit algorithm can outperform the best result of the FERET'97 evaluation by a large margin. In order to further improve the face recognition accuracy, we propose to fuse the generic model and identity-specific model for face recognition. The new method achieves over 90% accuracy on the FERET duplicate probe set, which is the highest accuracy reported on this challenging probe set.(4) Face recognition technology is of great significance for applications involving national security and crime prevention. Despite enormous progress in this field, machine-based system is still far from the goal of matching the versatility and reliability of human face recognition. In this paper, we show that a simple system designed by emulating biological strategies of human visual system can largely surpass the state-of-the-art performance on uncontrolled face recognition. In particular, the proposed system integrates dual retinal texture and color features for face representation, an incremental robust discriminant model for high level face coding, and a hierarchical cue-fusion method for similarity qualification. We demonstrate the strength of the system on the large-scale face verification task following the evaluation protocol of the FRGC version 2 Experiment 4. The results are surprisingly well:Its modules significantly outperform their state-of-the-art counterparts, such as Gabor image representation, local binary patterns, and enhanced Fisher model. Furthermore, the integrated system reduces the recognition error rate by 71.3 percent over the FRGC 2005 best result.In summary, the algorithm proposed in this paper is simple but effective, with clear theoretical meaning. They achieve excellent on FERET and FRGC experiments, the two most major evaluation in the literature, which clear show the core ideas of this paper is highly applicable in the real world settings, worth further research.