Hakan Cevikalp

Prof. Dr. Hakan Cevikalp

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Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 January 2005 in IEEE Transactions on Pattern Analysis and Machine Intelligence
Verified
Journal Article
Closed access
HCHakan Cevikalp
Hakan Cevikalp
MNM. Neamtu
M. Neamtu
MWM. Wilkes
M. Wilkes
ABA. Barkana
A. Barkana
In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is known as the "small sample size" problem. In this paper, we propose a new face recognition method called the discriminative common vector method based on a variation of Fisher's linear discriminant analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's linear discriminant criterion given in the paper. Our test results show that the discriminative common vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.
1 June 2010
Verified
Conference Paper
Closed access
HCHakan Cevikalp
Hakan Cevikalp
BTBill Triggs
Bill Triggs
We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual's face, not just a single image, so recognition decisions need to be based on comparisons of image sets. Methods for this have two main aspects: the models used to represent the individual image sets; and the similarity metric used to compare the models. Here, we represent images as points in a linear or affine feature space and characterize each image set by a convex geometric region (the affine or convex hull) spanned by its feature points. Set dissimilarity is measured by geometric distances (distances of closest approach) between convex models. To reduce the influence of outliers we use robust methods to discard input points that are far from the fitted model. The kernel trick allows the approach to be extended to implicit feature mappings, thus handling complex and nonlinear manifolds of face images. Experiments on two public face datasets show that our proposed methods outperform a number of existing state-of-the-art ones.
7 July 2016 in IEEE Transactions on Pattern Analysis and Machine Intelligence
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Journal Article
Closed access
HCHakan Cevikalp
Hakan Cevikalp
In this paper, we propose novel methods that are more suitable than classical large-margin classifiers for open set recognition and object detection tasks. The proposed methods use the best fitting hyperplanes approach, and the main idea is to find the best fitting hyperplanes such that each hyperplane is close to the samples of one of the classes and is as far as possible from the other class samples. To this end, we propose two different classifiers: The first classifier solves a convex quadratic optimization problem, but negative samples can lie on one side of the best fitting hyperplane. The second classifier, however, allows the negative samples to lie on both sides of the fitting hyperplane by using concave-convex procedure. Both methods are extended to the nonlinear case by using the kernel trick. In contrast to the existing hyperplane fitting classifiers in the literature, our proposed methods are suitable for large-scale problems, and they return sparse solutions. The experiments on several databases show that the proposed methods typically outperform other hyperplane fitting classifiers, and they work as good as the SVM classifier in classical recognition tasks. However, the proposed methods significantly outperform SVM in open set recognition and object detection tasks.
1 April 2020 in Pattern Recognition
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Journal Article
Closed access
HCHakan Cevikalp
Hakan Cevikalp
BBBurak Benligiray
Burak Benligiray
OGOmer Nezih Gerek
Omer Nezih Gerek
13 November 2006 in IEEE Transactions on Neural Networks
Verified
Journal Article
Closed access
HCHakan Cevikalp
Hakan Cevikalp
MNMarian Neamtu
Marian Neamtu
MWMitch Wilkes
Mitch Wilkes
In some pattern recognition tasks, the dimension of the sample space is larger than the number of samples in the training set. This is known as the "small sample size problem". Linear discriminant analysis (LDA) techniques cannot be applied directly to the small sample size case. The small sample size problem is also encountered when kernel approaches are used for recognition. In this paper, we attempt to answer the question of "How should one choose the optimal projection vectors for feature extraction in the small sample size case?" Based on our findings, we propose a new method called the kernel discriminative common vector method. In this method, we first nonlinearly map the original input space to an implicit higher dimensional feature space, in which the data are hoped to be linearly separable. Then, the optimal projection vectors are computed in this transformed space. The proposed method yields an optimal solution for maximizing a modified Fisher's linear discriminant criterion, discussed in the paper. Thus, under certain conditions, a 100% recognition rate is guaranteed for the training set samples. Experiments on test data also show that, in many situations, the generalization performance of the proposed method compares favorably with other kernel approaches
4 February 2023 in Pattern Recognition
Verified
Journal Article
Closed access
HCHakan Cevikalp
Hakan Cevikalp
BUBedirhan Uzun
Bedirhan Uzun
YSYusuf Salk
Yusuf Salk
HSHasan Saribas
Hasan Saribas
OKOkan Köpüklü
Okan Köpüklü