Goksel Gunlu

Bio: Goksel Gunlu is an academic researcher from Gazi University. The author has contributed to research in topics: Facial recognition system & Discrete cosine transform. The author has an hindex of 4, co-authored 10 publications receiving 53 citations.

Feature extraction and discriminating feature selection for 3D face recognition

Abstract: This paper presents a 3D face recognition method In this method, 3D Discrete Cosine Transform (DCT) is used to extract features Before the feature extraction, faces are aligned with respect to nose tip and then registered two times: according to average nose and average face Then the coefficients of 3D transformation are calculated The most discriminating 3D transform coefficients are selected as the feature vector where the ratio of between-class variance and within-class variance is used for discriminant coefficient selection The results show that the most energetic features, low frequency components, are not the most discriminating features The method was also modified based on 3D Discrete Fourier Transform (DFT) for feature selection as regarding real and complex DFT coefficients as independent features Discriminating features were matched by using the Nearest Neighbor classifier Recognition experiments were realized on 3D RMA face database The proposed method yileds a recognition rate above 99% for 3D DCT based features

Face Recognition with Discriminating 3D DCT Coefficients

Abstract: In this study, we investigate the use of a 3D discrete cosine transform (DCT) for 3D face recognition and present a novel 3D DCT-based feature extraction method with the selection of discriminating coefficients. We apply a 3D DCT on the voxel data, and use transform coefficients as features. Then the most discriminating 3D transform coefficients are selected with the proportion of variance, sequential floating forward selection and sequential floating backward selection methods. After feature selection, the linear discriminant analysis is applied on reduced sized feature vectors. We compare the results of different feature selection methods and show that a hybrid feature selection method has the best performance both in terms of time and recognition. Our experimental results verify that the discriminating DCT coefficients increase the face recognition rate more than the low-indexed coefficients do. On the other hand, the discriminating coefficients have only an energy level of 1.58%, too low when compared with the total energy of low-indexed coefficients. This fact shows that the discriminating coefficients are not the most energetic ones. With these coefficients, a recognition rate of 99.25% is achieved and this result is compared with other methods tested on a 3D RMA face database.

3D Face Decomposition and Region Selection Against Expression Variations

Abstract: 3D face recognition exploits shape information as well as texture information in 2D systems. The use of whole 3D face is sensitive to some undesired situations like expression variations. To overcome this problem, we investigate a new approach that decomposes the whole 3D face into sub-regions and independently extracts features from each sub-region. 3D DCT is applied to each sub-region and most discriminating DCT coefficients are selected. The nose region gives the most contribution to the list of discriminating coefficients. Furthermore, a better recognition rate is achieved by only using the nose region. The highest recognition score in our experiments is 98.97% where rank-one recognition rates are considered. The results of the proposed approach are compared to other methods that use FRGC v2 database.

Symmetry analysis for 2D images by using DCT coefficients

Abstract: In this study, we proposed a new method to align symmetric signals by using symmetry property of Discrete Cosine Transform (DCT), which is widely used in signal compression and pattern recognition. For the symmetric signals, the energy is concentrated in the even indexed DCT coefficients. Using this property, we defined a symmetry measure. In this measure, ratio of the energy in even indexed coefficients to total energy gives the symmetry value for the signal. When the symmetry values of the rotated image at different angles become maximum, it means that the image is aligned according to its symmetry axis. We use 2D face data in experimental studies, because of its well-known symmetric property. This symmetry measure can also be adapted to any dimensional signals. The proposed method is tested on texture and shape data of the 3D face which is taken from FRGC database. Using the proposed method which exploits face symmetry, it is shown that the alignment resolution better than 1° can be achieved.

Driver Face Recognition by Using 3D Discrete Cosine Transform

Abstract: Next generation vehicles are foreseen to use biometric recognition systems for person authentication. One of them is face recognition. In this study, a different approach of feature vector generation is presented for a face recognition system. To generate the feature vectors, DCTfaces, which are very similar to Eigenfaces, are used. DCTfaces are obtained by using 3D-DCT of face images after an image synthesis and ID-Inverse DCT of the 3D DCT coefficient layers. Taking the advantage of DCT image compression capability, very few terms are used for these DCTfaces. Experimental results show that, DCTfaces can be successfully used for feature vector generation in face recognition.

Aesthetic 3D model evolution

Abstract: A new research frontier for evolutionary 2D image generation is the use of mathematical models of aesthetics, with the goal of automatically evolving aesthetically pleasing images. This paper investigates the application of similar models of aesthetics towards the evolution of 3-dimensional structures. We extend existing models of aesthetics used for image evaluation to the 3D realm, by considering quantifiable properties of surface geometry. Analyses used include entropy, complexity, deviation from normality, 1/f noise, and symmetry. A new 3D L-system implementation promotes accurate analyses of surface features, as well as productive rule sets when used with genetic programming. Multi-objective evaluation reconciles multiple aesthetic criteria. Experiments resulted in the generation of many models that satisfied multiple criteria. A human survey was conducted, and survey takers showed a statistically significant preference for high-fitness highly-evolved models over low-fitness unevolved ones. This research shows that aesthetic evolution of 3D structures is a promising new research area for evolutionary design.

3D Face Modeling, Analysis and Recognition

Abstract: 3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further.The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied.Key features: Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images Contains numerous exercises and algorithms throughout the book

Real-time and low-power processing of 3D direct/inverse discrete cosine transform for low-complexity video codec

Abstract: The 3D discrete cosine transform and its inverse (3D DCT/IDCT) extend the spatial compression properties of conventional 2D DCT to the spatio-temporal coding of 2D videos The 3D DCT/IDCT transform is particularly suited for embedded systems needing the low-complexity implementation of both video encoder and decoder, such as mobile terminals with video-communication capabilities This paper addresses the problem of real-time and low-power 3D DCT/IDCT processing by presenting a context-aware fast transform algorithm and a family of VLSI architectures characterized by different levels of parallelism Implemented in submicron CMOS technology, the proposed hardware macrocells support the real-time processing of main video formats (up to high definition ones with an input rate of tens of Mpixels/s) with different trade-offs between circuit complexity, power consumption and computational throughput Voltage scaling and adaptive clock-gating strategies are applied to reduce the power consumption versus the state of the art