Aneesh Krishna

Using Kinect for face recognition under varying poses, expressions, illumination and disguise

We present an algorithm that uses a low resolution 3D sensor for robust face recognition under challenging conditions. A preprocessing algorithm is proposed which exploits the facial symmetry at the 3D point cloud level to obtain a canonical frontal view, shape and texture, of the faces irrespective of their initial pose. This algorithm also fills holes and smooths the noisy depth data produced by the low resolution sensor. The canonical depth map and texture of a query face are then sparse approximated from separate dictionaries learned from training data. The texture is transformed from the RGB to Discriminant Color Space before sparse coding and the reconstruction errors from the two sparse coding steps are added for individual identities in the dictionary. The query face is assigned the identity with the smallest reconstruction error. Experiments are performed using a publicly available database containing over 5000 facial images (RGB-D) with varying poses, expressions, illumination and disguise, acquired using the Kinect sensor. Recognition rates are 96.7% for the RGB-D data and 88.7% for the noisy depth data alone. Our results justify the feasibility of low resolution 3D sensors for robust face recognition.

A neural network approach for the prediction of in vitro culture parameters for maximum biomass yields in hairy root cultures.

The present study deals with ANN based prediction of culture parameters in terms of inoculum density, pH and volume of growth medium per culture vessel and sucrose content of the growth medium for Glycyrrhiza hairy root cultures. This kind of study could be a model system in exploitation of hairy root cultures for commercial production of pharmaceutical compounds using large bioreactors. The study is aimed to evaluate the efficiency of regression neural network and back propagation neural network for the prediction of optimal culture conditions for maximum hairy root biomass yield. The training data for regression and back propagation networks were primed on the basis of function approximation, where final biomass fresh weight (f(wt)) was considered as a function of culture parameters. On this basis the variables in culture conditions were described in the form of equations which are for inoculum density: y=0.02x+0.04, for pH of growth medium: y=x+2.8, for sucrose content in medium: y=9.9464x+(-9.7143) and for culture medium per culture vessel: y=10x. The fresh weight values obtained from training data were considered as target values and further compared with predicted fresh weight values. The empirical data were used as testing data and further compared with values predicted from trained networks. Standard MATLAB inbuilt generalized regression network with radial basis function radbas as transfer function in layer one and purelin in layer two and back propagation having purelin as transfer function in output layer and logsig in hidden layer were used. Although in comparative assessment both the networks were found efficient for prediction of optimal culture conditions for high biomass production, more accuracy in results was seen with regression network.

Co-evolution of complementary formal and informal requirements

Agent-oriented Conceptual Modelling (AoCM, as exemplified by the i* notation by E. Yu (1995)), represents an interesting approach to modelling early phase requirements that is particularly effective in capturing organizational contexts, stake-holder intentions and rationale. There are significant benefits in using formal methods for the development of computer systems and improving their quality. We propose a methodology which permits the use of these two otherwise disparate approaches in a complementary and synergistic fashion for requirements engineering.

Supporting quantitative reasoning of non-functional requirements: a process-oriented approach

A long standing problem in software engineering is inadequate requirements elicitation, analysis, specification, validation and management. The lack of well defined requirements is one of the major causes of project failure. Several well-known techniques and frameworks have been developed to deal with the functional aspect of requirements engineering. Recent years have also seen the emergence of frameworks that incorporate non-functional requirements. The Non-Functional Requirements (NFR) Framework models non-functional requirements and associated implementation methods. This paper presents a process-orientated, lightweight, quantitative extension to the NFR Framework; focusing on providing quantitative support to the decision process and how decisions affect the system.

Face recognition using various scales of discriminant color space transform

Research on color face recognition in the existing literature is aimed to establish a color space that can have the most of the discriminative information from the original data. This mainly includes optimal combination of different color components from the original color space. Recently proposed discriminate color space (DCS) is theoretically optimal for classification, in which one seeks a set of optimal coefficients in terms of linear combinations of the R, G and B components (based on a discriminate criterion). This work proposes an innovative block-wise DCS (BWDCS) method, which allows each block of the image to be in a distinct DCS. This is an interesting alternative to the methods relying on converting whole image to DCS. This idea is evaluated with four appearance-based subspace state-of-the-art methods on five different publicly available databases including the well-known FERET and FRGC databases. Experimental results show that the performance of these four gray-scale based methods can be improved by 17% on average when they are used with the proposed color space.

A Pragmatic GIS-Oriented Ontology for Location Based Services

With advances in automatic position sensing and wireless connectivity, location-based services (LBS) are rapidly developing, particularly in fields of geographic, tourism and logistic information systems. Currently, Web service has been viewed as one of most significant innovations in business industry, and designed on demand to provide spatial related information for LBS consumption. However, the traditional Web Service Description Language (WSDL) cannot meet those requirements, as WSDL is not able to support semantic content and information. In recent years, Ontology came up with an effective approach to enhance service description, automated discovery, dynamic composition, enactment, and other tasks such as managing and using service-based systems. In this paper, we propose geographic ontology based on Geography Markup Language (GML) and extend OWL-S profile to form geographic profile. Web service, which is advertised on the basis of our GeoProfile, contains geographic information inherently.

Robust Face Recognition by Utilizing Color Information and Sparse Representation

In this paper, we consider the problem of robust face recognition using color information. In this context, sparse representation-based algorithms are the state-of-the-art solutions for gray facial images. We will integrate the existing sparse representation-based algorithms with color information and this integration can improve the previous performances significantly. Furthermore, we propose a new performance metric, namely the discriminativeness (DIS) to describe the recognition effectiveness for sparse representation algorithms. We find out that the richer information in color space can be used to increase the DIS, i.e. enhancing the robustness in face recognition. Extensive experiments have been conducted under different conditions, including various feature extractors, random pixel corruptions and occlusions on AR and GT databases, to demonstrate the advantages of using color information in robust face recognition. Detailed analysis is also included for each experiment to explain why and how color improve the robustness of different sparse representation-based methods.

Towards Optimising Non-functional Requirements

Non-functional requirements are an important, and often critical, aspect of any software system. However, determining the degree to which any particular software system meets such requirements and incorporating such considerations into the software design process is a difficult challenge. This paper presents a modification of the NFR framework that allows for the discovery of a set of system functionalities that optimally satisfice a given set of non-functional requirements. This new technique introduces an adaptation of softgoal interdependency graphs, denoted softgoal interdependency ruleset graphs, in which label propagation can be done consistently. This facilitates the use of optimisation algorithms to determine the best set of bottom-level operationalizing softgoals that optimally satisfice the highest-level NFR softgoals. The proposed method also introduces the capacity to incorporate both qualitative and quantitative information.

An Integrated Approach to Managing Business Process Risk Using Rich Organizational Models

Business processes represent the operational capabilities of an organization. In order to ensure process continuity, the effective management of risks becomes an area of key concern. In this paper we propose an approach for supporting risk identification with the use of higher-level organizational models. We provide some intuitive metrics for extracting measures of actor criticality and vulnerability from organizational models. This helps direct risk management to areas of critical importance within organization models. Additionally, the information can be used to assess alternative organizational structures in domains where risk mitigation is crucial. At the process level, these measures can be used to help direct improvements to the robustness and failsafe capabilities of critical or vulnerable processes. We believe our novel approach, will provide added benefits when used with other approaches to risk management during business process management, that do not reference the greater organizational context during risk assessment.

Ant Inspired Scalable Peer Selection in Ontology-Based Service Composition

This work focuses on proposing a method of effectively dealing with P2P-based service selection and composition, especially when handling a large number of Peers along with their diverse qualities. The QoS-aware Peer selection is one of the major challenges faced in order to guarantee the success and enhance performance of distributed computing. Since many Peer candidates provide overlapping or identical functionalities, though with different QoS evaluations, selections need to be rapidly conducted to determine which Peers are suitable to join in the requested composite service. The main contribution of this paper is proposing a P2P-based service selection model, in which Peer’s non-functional properties are modeled with Web Service Modelling Ontology (WSMO), and where Ant Colony Optimisation (ACO) technique is adopted to facilitate and enhance the QoS-aware Peers’ composition. We present experimental results to illustrate the effectiveness and feasibility of the proposed method.