Ear Identification by Fusion of Segmented Slice Regions using Invariant Features: An Experimental Manifold with Dual Fusion Approach

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. The proposed ear identification method makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are then detected and extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors which are used for identification of individuals separately. The proposed identification technique is tested on IIT Kanpur ear database of 400 individuals and is found to achieve 98.25% accuracy for identification while top 5 matched criteria is set for each subject.Comment: 12 pages, 3 figure

Performance Comparison of PSO and Its New Variants in the Context of VLSI Global Routing

Substantial reduction of gate delay occurred in recent times owing to radical decrement of transistor size. The interconnect length and delay are accordingly increased owing to the exponential escalation of packaging density with additional transistors being fabricated on the same chip area. The function of VLSI routing that seems to be more defying to the scholars, is categorized in global routing and detailed routing phase. In global routing phase, the prevalent method to lessen the wire length for reducing interconnect delay is to adjust the cost of the Steiner tree, devised by the terminal nodes to be interconnected. Nevertheless, Steiner tree problem is a NP-complete problem in classical graph theory where meta-heuristics might impart beneficial elucidations. Particle swarm optimization (PSO) is a robust algorithm concerning VLSI routing field. This chapter is regarding the proposal of a self-adaptive mechanism for monitoring acceleration coefficient of PSO and evaluating its functionalities with the existing acceleration coefficient controlled PSO in numerous allocation topologies of terminal nodes within definite VLSI layout. The outcomes of PSO variant with constriction factor in context to VLSI route reduction ability and robustness are also inspected. Additionally, a new effort in adapting the PSO with embracement of genetic algorithm is established

A Comparative Performance Study of Hybrid SET-CMOS Based Logic Circuits for the Estimation of Robustness

The urge of inventing a new low power consuming device for the post CMOS future technology has drawn the attention of the researchers on Single Electron Transistor [SET]. The two main virtues, ultra low power consumption [1] and ultra small dimension of SET [12, 13] have stimulated the researchers to consider it as a possible alternative. In our past paper [1] we have designed and simulated some basic gates. In this paper we have designed and simulated hybrid SET-CMOS based counter circuits, shift register to show that the hybrid SET-MOS based circuits consumes the lesser power than MOS based circuits. All the simulation were done and verified in Tanner environment using the MIB model for SET and the BSIM4.6.1 model for MOSFET. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3565

Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.Comment: 7 pages, 6 figures, IEEE Computer Vision and Pattern Recognition Workshop on Biometric