Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

A novel face recognition method based on hypersausage neural networks

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Hypersausage neural networks and its application in face recognition

In this paper, we firstly give the nature of 'hypersausages', study its structure and training of the network, then discuss the nature of it by way of experimenting with ORL face database, and finally, verify its unsurpassable advantages compared with other means.

Molecular cloning and mRNA expression of peptidoglycan recognition protein (PGRP) gene in bay scallop (Argopecten irradians, Lamarck 1819)

Peptidoglycan recognition proteins (PGRPs) are a type of pattern recognition molecules (PRM) that recognize the unique cell wall component peptidoglycan (PGN) of bacteria and are involved in innate immunity. The first bivalve PGRP cDNA sequence was cloned from bay scallop Argopecten irradians by expressed sequence tag (EST) and PCR technique. The full-length cDNA of bay scallop PGRP (designated AiPGRP) gene contained 10 18 bp with a 615-bp open reading frame that encoded a polypeptide of 205 amino acids. The predicted amino acid sequence of AiPGRP shared high identity with PGRP in other organisms, such as PGRP precursor in Trichoplusia ni and PGRP SC2 in Drosophila melanogaster. A quantitative reverse transcriptase Real-Time PCR (qRT-PCR) assay was developed to assess the mRNA expression of AiPGRP in different tissues and the temporal expression of AiPGRP in the mixed primary cultured hemocytes challenged by microbial components lipopolyssacharide (LPS) from Escherichia coli and PGN from Micrococcus luteus. Higher-level mRNA expression of AiPGRP was detected in the tissues of hemocytes, gonad and kidney. The expression of AiPGRP in the mixed primary cultured hemocytes was up regulated after stimulated by PGN, while LPS from E. coli did not induce AiPGRP expression. The results indicated that AiPGRP was a constitutive and inducible expressed protein that was mainly induced by PGN and could be involved in scallop immune response against Gram-positive bacteria infection. (c) 2006 Elsevier Ltd. All rights reserved.

An ancient C-type lectin in Chlamys farreri (CfLec-2) that mediate pathogen recognition and cellular adhesion

C-type lectins are a superfamily of Ca2+ dependent carbohydrate-recognition proteins which play significant diverse roles in nonself-recognition and clearance of invaders. In the present study, a C-type lectin (CfLec-2) from Zhikong scallop Chlamys farreri was selected to investigate its functions in innate immunity. The mRNA expression of CfLec-2 in hemocytes was significantly up-regulated (P < 0.01) after scallops were stimulated by LPS. PGN or beta-glucan, and reached the highest expression level at 12h post-stimulation, which was 72.5-, 23.6- or 43.8-fold compared with blank group, respectively. The recombinant Cflec-2 (designated as rCfLec-2) could bind LPS, PGN, mannan and zymosan in vitro, but it could not bind beta-glucan. Immunofluorescence assay with polyclonal antibody specific for Cflec-2 revealed that CfLec-2 was mainly located in the mantle, kidney and gonad. Furthermore, rCfLec-2 could bind to the surface of scallop hemocytes, and then initiated cellular adhesion and recruited hemocytes to enhance their encapsulation in vitro, and this process could be specifically blocked by anti-rCfLec-2 serum. These results collectively suggested that CfLec-2 from the primitive deuterostome C. farreri could perform two distinct immune functions, pathogen recognition and cellular adhesion synchronously, while these functions were performed by collectins and selectins in vertebrates, respectively. The synchronous functions of pathogen recognition and cellular adhesion performed by CfLec-2 tempted us to suspect that CfLec-2 was an ancient form of C-type lectin, and apparently the differentiation of these two functions mediated by C-type lectins occurred after mollusk in phylogeny. (C) 2010 Elsevier Ltd. All rights reserved.

Peptidoglycan recognition protein of Chlamys farreri (CfPGRP-S1) mediates immune defenses against bacterial infection

Peptidoglycan recognition protein (PGRP) is an essential molecule in innate immunity for both invertebrates and vertebrates, owing to its prominent ability in detecting and eliminating the invading bacteria. Several PGRPs have been identified from mollusk, but their functions and the underlined mechanism are still unclear. In the present study, the mRNA expression profiles, location, and possible functions of PGRP-S1 from Zhikong scallop Chlamys farreri (CfPG RP-St) were analyzed. The CfPGRP-S1 protein located in the mantle, gill, kidney and gonad of the scallops. Its mRNA expression in hemocytes was up-regulated extremely after PGN stimulation (P < 0.01), while moderately after the stimulations of LPS (P < 0.01) and beta-glucan (P < 0.05). The recombinant protein of CfPGRP-S1 (designated as rCfPGRP-S1) exhibited high affinity to PGN and moderate affinity to LPS, but it did not bind beta-glucan. Meanwhile, rCfPGRP-S1 also exhibited strong agglutination activity to Gram-positive bacteria Micrococcus luteus and Bacillus subtilis and weak activity to Gram-negative bacteria Escherichia coli. More importantly, rCfPGRP-S1 functioned as a bactericidal amidase to degrade PGN and strongly inhibit the growth of E. coli and Staphyloccocus aureus in the presence of Zn2+. These results indicated that CfPGRP-S1 could not only serve as a pattern recognition receptor recognizing bacterial PGN and LPS, but also function as a scavenger involved in eliminating response against the invaders. (C) 2010 Elsevier Ltd. All rights reserved.

Face Recognition Under Varying Pose

While researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing faces for the last 20 years, most systems specialize on frontal views of the face. We present a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modelling views per person.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate

Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Neural mechanisms of context effects on face recognition: automatic binding and context shift decrements.

Although people do not normally try to remember associations between faces and physical contexts, these associations are established automatically, as indicated by the difficulty of recognizing familiar faces in different contexts ("butcher-on-the-bus" phenomenon). The present fMRI study investigated the automatic binding of faces and scenes. In the face-face (F-F) condition, faces were presented alone during both encoding and retrieval, whereas in the face/scene-face (FS-F) condition, they were presented overlaid on scenes during encoding but alone during retrieval (context change). Although participants were instructed to focus only on the faces during both encoding and retrieval, recognition performance was worse in the FS-F than in the F-F condition ("context shift decrement" [CSD]), confirming automatic face-scene binding during encoding. This binding was mediated by the hippocampus as indicated by greater subsequent memory effects (remembered > forgotten) in this region for the FS-F than the F-F condition. Scene memory was mediated by right parahippocampal cortex, which was reactivated during successful retrieval when the faces were associated with a scene during encoding (FS-F condition). Analyses using the CSD as a regressor yielded a clear hemispheric asymmetry in medial temporal lobe activity during encoding: Left hippocampal and parahippocampal activity was associated with a smaller CSD, indicating more flexible memory representations immune to context changes, whereas right hippocampal/rhinal activity was associated with a larger CSD, indicating less flexible representations sensitive to context change. Taken together, the results clarify the neural mechanisms of context effects on face recognition.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.