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.

Abstract:The susceptibility of complex diseases are characterised by numerous genetic, lifestyle, and environmental causes individually or due to their interaction effects. The recent explosion in detecting genetic interacting factors is increasingly revealing the underlying biological networks behind complex diseases. Several computational methods are explored to discover interacting polymorphisms among unlinked loci. However, there has been no significant breakthrough towards solving this problem because of biomolecular complexities and computational limitations. Our previous research trained a deep multilayered feedforward neural network to predict two-locus polymorphisms due to interactions in genome-wide data. The performance of the method was studied on numerous simulated datasets and a published genomewide dataset. In this manuscript, the performance of the trained multilayer neural network is validated by varying the parameters of the models under various scenarios. Furthermore, the observations of the previous method are confirmed in this study by evaluating on a real dataset. The experimental findings on a real dataset show significant rise in the prediction accuracy over other conventional techniques. The result shows highly ranked interacting two-locus polymorphisms, which may be associated with susceptibility for the development of breast cancer.

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The premise behind 'third wave' Business Process Management (BPM1) is effective support for change at levels. Business Process Modeling (BPM2) notations such as BPMN are used to effectively conceptualize and communicate process configurations to relevant stakeholders. In this paper we argue that the management of change throughout the business process model lifecycle requires greater conceptual support achieved via a combination of complementary notations. As such the focus in this paper is on the co-evolution of operational (BPMN) and organizational (i*) models. Our intent is to provide a way of expressing changes, which arise in one model, effectively in the other model. We present constrained development methodologies capable of guiding an analyst when reflecting changes from an i* model to a BPMN model and vice-versa. 2 ) notations such as BPMN are used to effectively conceptualize and communicate process configurations to relevant stakeholders. In this paper we argue that the management of change throughout the business process model lifecycle requires greater conceptual support achieved via a combination of complementary notations. As such the focus in this paper is on the co-evolution of operational (BPMN) and organizational (i*) models. Our intent is to provide a way of expressing changes, which arise in one model, effectively in the other model. We present constrained development methodologies capable of guiding an analyst when reflecting changes from an i* model to a BPMN model and vice-versa.

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