In this paper, we introduce a novel shape-based interest point (SIP) descriptor to encode local surface shapes for three-dimensional (3D) ear recognition; the descriptor provides an advantage over previous descriptors by capturing greater details of the macro-shape patterns surrounding an interest point. Using the SIP descriptor, a function is developed to measure the shape dissimilarity between any two interest points. Finally, in the recognition stage, a probe and a gallery pair are compared by applying the matching algorithm on the interest points, with the similarity score set as the number of matched interest points. The proposed method has been tested on the University of Notre Dame(UND) collection J2 dataset, containing range images of 415 subjects. The experimental results demonstrate that our method achieves a 97.4% rank-one recognition rate and a 2.0% Equal Error Rate (EER), which outperforms the state-of-the-art methods.

We present an efficient and robust system for landmark localization, segmentation and pose classification of ears from 3D profile facial range data. After defining 18 landmarks on the ear, including Triangular Fossa and Incisure Intertragica, a novel Ear Tree-structured Graph (ETG) is proposed to represent the 3D ear. We trained a flexible mixture model to locate these landmarks automatically. Afterwards, the ear region is outlined as the minimum rectangle including all landmarks. Finally, by calculating the turning angle between landmarks on the helix, the ear is classified as either a left or a right ear. To the best of our knowledge, there is no previous work on automatic landmark localization for 3D ear on 3D facial profile depth images. Experiments are conducted on University of Notre Dame Collection F and Collection J2 datasets, containing large occlusion, scale and pose variations. Results demonstrate the effectiveness of the proposed techniques.

SUMMARY: EcoGene.org is a genome database and website dedicated to Escherichia coli K-12 substrain MG1655 that is revised daily using information derived from the biomedical literature and in-house analysis. EcoGene is a major source of annotation updates for the MG1655 Genbank record, one of only a few Genbank genome records that are updated by a community effort. The Reference Sequence (RefSeq) database, built by The National Center for Biotechnology Information, comprises a set of duplicate Genbank genome records that can be modified by the NCBI staff annotators. EcoGene-RefSeq is being developed as a stand-alone internet resource to facilitate the usage of EcoGene-based tools on any of the >2400 completed prokaryotic genome records that are currently available at the RefSeq database. AVAILABILITY: The web interface of EcoGene-RefSeq is available at http://www.ecogene.org/refseq. CONTACT: [email protected] or [email protected].

We present a complete three-dimensional (3-D) ear recognition system combining local and holistic features in a computationally efficient manner. The system is comprised of four primary components, namely: 1) ear image segmentation; 2) local feature extraction and matching; 3) holistic feature extraction and matching; and 4) a fusion framework combining local and holistic features at the match score level. For the segmentation component, we introduce a novel shape-based feature set, termed the Histograms of Indexed Shapes (HIS), to localize a rectangular region containing the ear. For the local feature extraction and representation component, we extend the HIS feature descriptor to an object-centered 3-D shape descriptor, the Surface Patch Histogram of Indexed Shapes (SPHIS), for local ear surface representation and matching. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation from which an efficient, voxel-wise comparison of gallery-probe model pairs can be made. The match scores obtained from both the local and holistic matching components are fused to generate the final match scores. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing range images of 415 subjects yielded a rank-one recognition rate of 98.3% and an equal error rate of 1.7%. These results demonstrate that the proposed approach outperforms state-of-the-art 3-D ear biometric systems. Additionally, the method is considerably more efficient compared to the state-of-the-art because it employs a sparse set of features rather than using the dense model.

We present a novel voxelization framework for holistic Three-Dimensional (3D) object representation that accounts for distinct surface features. A voxelization of an object is performed by encoding an attribute or set of attributes of the surface region contained within each voxel occupying the space that the object resides in. To our knowledge, the voxel structures employed in previous methods consist of uniformly-sized voxels. The proposed framework, in contrast, generates structures consisting of variable-sized voxels that are adoptively distributed in higher concentration near distinct surface features. The primary advantage of the proposed method over its fixed resolution counterparts is that it yields a significantly more concise feature representation that is demonstrated to achieve a superior recognition performance. An evaluation of the method is conducted on a 3D ear recognition task. The ear provides a challenging case study be- cause of its high degree of inter-subject similarity.

In this paper, we present a robust method for 2D ear recognition using color SIFT features. Firstly, we extend the Scale Invariant Feature Transform (SIFT) algorithm originally performed on the intensity channel [1] to the RGB color channels to maximize the robustness of the SIFT feature descriptor. Secondly, a feature matching algorithm for ear recognition is proposed by fusion of the features extracted from the different color channels. Experiments conducted on the University of Notre Dame (UND) and the West Virginia University (WVU) ear biometric datasets indicate that our method can achieve better recognition rates than the state-of-the-art methods applied on the same datasets.

In this paper, we present a novel method for 3D face recognition using adaboosted geodesic distance features. Firstly, a generic model is finely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model. Adaboost then generates a strong-classifier based on a collection of geodesic distances that are most discriminative for face recognition. Experiments conducted on the face recognition grand challenge (FRGC) database D collection indicate that the system can achieve over a 95% rank-one recognition rate.

This paper describes Orchid, a system that converts declarative mapping specifications into data flow specifications (ETL jobs) and vice versa. Orchid provides an abstract operator model that serves as a common model for both transformation paradigms; both mappings and ETL jobs are transformed into instances of this common model. As an additional benefit, instances of this common model can be optimized and deployed into multiple target environments. Orchid is being deployed inFastTrack, a data transformation toolkit in IBM Information Server.