Helin Dutagaci

Hand biometrics

The potential of hand shape and hand texture-based biometry is investigated and algorithms are developed. Feature extraction stage is preceded by meticulous registration of the deformable shape of the hand. Alternative features addressing hand shape and hand texture are compared. Independent component analysis features prove to be the best performing in the identification and verification tasks. It is shown that hand biometric devices can be built that perform reliably for a population of at least 1000. 00.

Representation Plurality and Fusion for 3-D Face Recognition

In this paper, we present an extensive study of 3D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules and voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we have tried to find the answers to the following questions: 1) the relative importance of the face representation techniques vis-a-vis the types of features extracted; 2) the impact of the gallery size; 3) the conditions, under which subspace methods are preferable, and the compression factor; 4) the most advantageous fusion level and fusion methods; 5) the role of confidence votes in improving fusion and the style of selecting experts in the fusion; and 6) the consistency of the conclusions across different databases.

Comparative analysis of global hand appearance-based person recognition

We provide a survey of hand biometric techniques in the literature and incorporate several novel results of hand-based per- sonal identification and verification. We compare several feature sets in the shape-only and shape-plus-texture categories, empha- sizing the relevance of a proper hand normalization scheme in the success of any biometric scheme. The preference of the left and right hands or of ambidextrous access control is explored. Since the business case of a biometric device partly hinges on the longevity of its features and the generalization ability of its database, we have tested our scheme with time-lapse data as well as with subjects that were unseen during the training stage. Our experiments were con- ducted on a hand database that is an order of magnitude larger than any existing one in the literature.

A benchmark for best view selection of 3D objects

The best view selection corresponds to the task of automatically selecting the most representative view of a 3D model. In this paper, we describe a benchmark for evaluation of best view selection algorithms. The benchmark consists of the preferred views of 68 3D models provided by 26 human subjects. The data was collected using a web-based subjective experiment where the users were asked to select the most informative view of a 3D model. We provided a quantitative evaluation measure based on this ground truth data, and compared the performances of seven best view selection algorithms.

SHREC'09 track: structural shape retrieval on watertight models

The annual SHape REtrieval Contest (SHREC) measures the performance of 3D model retrieval methods for several different types of models and retrieval purposes. In this contest the structural shape retrieval track focuses on the retrieval of 3d models which exhibit a relevant similarity in the shape structure. Shape structure is typically characterised by features like protrusions, holes and concavities. It defines relationships in which components of the shape are connected.

Subspace methods for retrieval of general 3D models

In statistical shape analysis, subspace methods such as PCA, ICA and NMF are commonplace, whereas they have not been adequately investigated for indexing and retrieval of generic 3D models. The main roadblock to the wider employment of these methods seems to be their sensitivity to alignment, itself an ambiguous task in the absence of common natural landmarks. We present a retrieval scheme based comparatively on three subspaces, PCA, ICA and NMF, extracted from the volumetric representations of 3D models. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. We mitigate the ambiguity of pose normalization with continuous PCA coupled with the use of all feasible axis labeling and reflections. The performance of the subspace-based retrieval methods on Princeton Shape Benchmark is on a par with the state-of-the-art methods.

3D face recognition by projection-based methods

In this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.

View subspaces for indexing and retrieval of 3D models

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

Benchmarks, performance evaluation and contests for 3D shape retrieval

Benchmarking of 3D Shape retrieval allows developers and researchers to compare the strengths of different algorithms on a standard dataset. Here we describe the procedures involved in developing a benchmark and issues involved. We then discuss some of the current 3D shape retrieval benchmarks efforts of our group and others. We also review the different performance evaluation measures that are developed and used by researchers in the community. After that we give an overview of the 3D shape retrieval contest (SHREC) tracks run under the EuroGraphics Workshop on 3D Object Retrieval and give details of tracks that we organized for SHREC 2010. Finally we demonstrate some of the results based on the different SHREC contest tracks and the NIST shape benchmark.

Detection of humans from depth images

With the development of consumer depth sensors, research on human detection and tracking from depth images has gained momentum. Depth information facilitates the extraction of objects from the background, and enables localization of these objects in 3D space. In this work, we present a new dataset of depth images acquired from indoor environments, such as home, office, coffee shop, where people are present in a variety of poses. We propose a new method for detection of unmoving humans, and test our algorithm on our new dataset.