Ozlem Ozbudak

On ear biometrics

Today the most successful biometric based identification technologies such as fingerprint, iris, retina, palm and face recognition are used worldwide in both criminal investigations and high security facilities. These technologies are well-studied, but research shows they have many drawbacks which decrease the success of the methods applied. Ear images are not affected by emotional expression, illumination, aging, poses and alike. In this study principal component analysis (PCA), fisher linear discriminant analysis (FLDA), discriminative common vector analysis (DCVA), and locality preserving projections (LPP) were applied to ear images for personal identification. The error and hit rates of four algorithms were calculated by random subsampling and k-fold cross validation.

Protein motifs retrieval by SS terns occurrences

This paper describes a new approach to the analysis of protein 3D structure based on the Secondary Structure (SS) representation. The focus is here on structural motif retrieval. The strategy is derived from the Generalized Hough Transform (GHT), but considering as structural primitive element, the triplet of SSs. The triplet identity is evaluated on the triangle having the vertices on the SS midpoints, and is represented by the three midpoints distances. The motif is characterized by the complete set of triplets, so the Reference Table (RT) has a tuple for each triplet. Tuples contain, beside the discriminant component (the three edge lengths), the mapping rule, i.e. the Reference Point (RP) location referred to the triplet. In the macromolecule to be analyzed, each possible triplet is searched in the RT and every match gives a contribution to a candidate location of the RP. Presence and location of the searched motif are certified by the collection of a number of contribution equal (obviously in absence of noise and ambiguities) to the RT cardinality (i.e. the number of motif triplets). The approach is tested on twenty proteins selected randomly from the PDB, but having a different number of SSs ranging from 14 to 46. The retrieval of all possible structural blocks composed by three, four and five SSs (very compact and completely distributed) have been conducted. The results show valuable performances for precision and computation time.

Effects of the facial and racial features on gender classification

This paper presents an experimental study on examining the effects of facial and racial features on gender classification. In order to show which facial feature is the most influential for gender classification, parts of several face images, such as, forehead, eyebrows, eyes, nose, lip and chin were masked. For dimension reduction, Principal Component Analysis (PCA) and for determination of gender, Fisher Linear Discriminant (FLD) algorithms were applied to masked face images. Moreover, the effects of racial features on gender classification were studied. Experimental results indicated that the nose is the most influential part for gender classification. Furthermore the gender of the Asian people is more easily distinguished than that of the people of African origin.

Search of protein structural blocks through secondary structure triplets

This paper presents an approach for protein motif retrieval founded on protein secondary structures (SSs) in 3D. This is a new way to analyze the protein 3D structure. In this approach, based on the Generalized Hough Transform (GHT), the primitives are the triangles defined by the midpoints of three SSs. The three distances between each SSs couple are used in searching and in the voting process. The barycenter of the motif is assigned as the Reference Point (RP). All motif triangles are compared with all possible triangles in the macromolecule. The lengths of triangle edges are used as selective parameters. For every correspondence a vote is given to the point which is figured out as motif barycenter with a special mapping rule and the point having most votes is determined as candidate RP. In this paper we made some experiments for retrieval of four- and five-SSs motif from the macromolecule. Experimental results showed that the RP is determined with precision and this new approach to retrieve the motif is simple to implement, computationally efficient and fast.

Searching Structural Blocks by SS Exhaustive Matching

This paper presents motif retrieval from a macromolecule or a protein by using structure comparison in 3D through an exhaustive matching analysis of secondary structures. The comparison is based on three parameters: midpoint distance (Md), axis distance (Ad) and angle (ϕ) related to a couple of SSs in 3D space. The barycenter of the motif is assigned as Reference Point (RP) and in order to find the RP related to every possible motif (instance) in the macromolecule a voting process is performed. The searched motif is compared with all possible instances having the same number of motif SSs in the macromolecule and gives a vote to the candidate barycenter for every correspondence. The point, which has the maximum number of votes, is determined as candidate RP. In this paper motifs composed by four and five secondary structures are searched. Experimental results show a good accuracy in determining the RP and hence in the retrieval of the searched motif.

Protein structural motifs search in protein data base

This research develops approaches and strategies in Bioinformatics with explicit attention to applications of Computational Biology. The plan is to develop structural motifs retrieval into the protein database (PDB). The aim here is of exploiting the Generalized Hough Transform for the comparison and the search of structural similarity. Note that, if the searched structure is just a component of a protein (like a structural motif or a domain) the same method supports the detection and the statistical distribution of these components, possibly answering questions of permanence among different species and phylogenetic questions related to biological homology.

Comparison of GHT-Based Approaches to Structural Motif Retrieval

The structure of a protein gives important information about its function and can be used for understanding the evolutionary relationships among proteins, predicting protein functions, and predicting protein folding. A structural motif is a compact 3D protein block referring to a small specific combination of secondary structural elements which appears in a variety of molecules. In this paper we present a comparison between few approaches for motif retrieval based on the Generalized Hough Transform (GHT). Performance comparisons, in terms of precision and computation time, are presented considering the retrieval of motifs composed by three to five SSs for more than 15 million searches. The approaches object of this study can be easily applied to the retrieval of greater blocks, up to protein domains, or even entire proteins.