16464835
Automated Face Pose Estimation Using Elastic Energy Models
Face pose estimation forms an important part in a face recognition system. However, fully automated and accurate pose determination still remains an unsolved problem in the research community. In this paper, we propose a novel elastic energy model to automatically estimate face poses. Our method employs statistical energy contributions of a set of feature points, which can avoid over-trusting selected anchor points. It provides a robust solution to the feature localisation inaccuracy problem, which is inevitable in practical applications with cluttered backgrounds. As a general configuration, our model can be easily implemented and extended to other non-rigid objects. Its effectiveness and robustness are revealed in our experiments
2955067
Single model face database retrieval by directional corner points
This paper presents a new face image retrieval approach using directional corner points (DCPs). Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching scheme suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be concerned. This research demonstrates that the proposed DCP approach provides a new solution, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.
41738297
Human face profile recognition using attributed string
A new attributed string matching method for human face profile recognition is proposed in this work. It is a novel idea to apply structural and syntactic technique on face profile matching. The approach works on a chain of profile line segments and highlights the favor of curve matching by suppressing the operations of insert and delete. The technique relies mainly on the merge and change operations of string to tackle the inconsistency problem of feature point detection. A quadratic penalty function is proposed to prohibit large angle changes and overmerging. The method produces very encouraging results and is found to be suitable for similar shape classification.
16621894
Feature-level fusion in personal identification
The existing studies of multi-modal and multi-view personal identification focused on combining the outputs of multiple classifiers at the decision level. In this study, we investigated the fusion at the feature level to combine multiple views and modals in personal identification. A new similarity measure is proposed, which integrates multiple 2D view features representing a visual identity of a 3D object seen from different viewpoints and from different sensors. The robustness to non-rigid distortions is achieved by the proximity correspondence manner in the similarity computation. The feasibility and capability of the proposed technique for personal identification were evaluated on multiple view human faces and palmprints. This research demonstrates that the feature-level fusion provides a new way to combine multiple modals and views for personal identification.
975623
Face Recognition by Combining Kernel Associative Memory and Gabor Transforms
Kernel associative memory (KAM) has previously been proposed as an efficient scheme for face recognition. In this paper, a hybrid method of combining KAM and Gabor wavelet transform is proposed. In this method, face images of each person are first decomposed into their spatial/frequency domains by Gabor transforms, which are then modelled by a KAM. While Gabor properties of orientation selectivity and spatial frequency selectivity provide discriminating features, KAM offers the means to capture the important intra-class variations. Experimental results obtained on two standard face databases demonstrated that the proposed method consistently improved the system performance
45735125
Robust visual similarity retrieval in single model face databases
In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.
32735043
Mining Web Sequential Patterns Using Reinforcement Learning
In this paper, the problem of discovering sequential patterns from the Web log is discussed and an algorithm of sequential patterns mining is brought forward. First, the data in the Web server log file is cleaned and the temporal set about every user is constructed. After the analysis of the temporal set, Markov decision process is applied to model Web log. Then, the sequential patterns of user behaviors are mined by means of reinforcement learning technology. Finally the experiment shows that our mining algorithm is effective.
15420304
Incremental gradient descent imputation method for missing data in learning classifier systems
Learning with incomplete or missing data has been a major challenge in learning classifier system. One method for covering missing data is imputing missing values based on the statistic of known values. Another is marking them matching arbitrary case. A new approach using incremental gradient descent imputation model is proposed in this paper, which use the relationship among variables to estimate the missing value. And, some experiments are conducted in order to compare the performance of new approach and other classical covering methods.
10844784
Meta-game Equilibrium for Multi-agent Reinforcement Learning
This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.
18781820
QSAR in grossly underdetermined systems: Opportunities and issues
Regression in grossly underdetermined systems has emerged as an important means for understanding molecular activity via comparative molecular field analysis (CoMFA) and other quantitative structure activity relationship (QSAR) studies. But this methodology has applications in much broader areas; for example, near-infrared spectroscopy, mutational enzyme activity studies including protein folding rates to determine which sites are important for determining conformation, and analyses of gene expression data from chip arrays. An error analysis which answers questions concerning the quality of the predictivity, the relative importance of each descriptor, the quality of the estimates of the contribution by each descriptor, and the number of independent components expressed by the associated data is indispensable in understanding whether some particular set of structure variables is important in defining the mechanisms driving the chemical or biological activities. This paper reviews opportunities for QSAR studies. It also considers the analytical aspects of error analysis in least-squares regression, and contrasts principal component regression (PCR) and partial least-squares (PLS) procedures with cross-validation on the issues of error analysis (e.g., the quality of the contribution estimates for each structure descriptor). Further, a methodology for selecting optimal subsets of components in PCR is presented.
5391334
Sequence homology detection through large scale pattern discovery
Extended Abstract We describe a new approach for idenhifling sequence similatity between a query sequence and a data base of proteins. The central idea is the use of a set ofpattems obtainedfiom the underlying data base through an one-time computation. These patterns are subsequently searchedfor on every query sequence presented to the system. A pattern matched by a region of the quev pinpoints to a potential local similarity between that reg+on and all the data base sequences also matching that pattern. By using a set of prudently chosen patterns, the tool presented in this work is able to discover weak but biologically important similarities.