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Articles 1 - 23 of 23
Full-Text Articles in Physical Sciences and Mathematics
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
Journal of Digital Forensics, Security and Law
Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going …
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
With the recent developments in technology, there has been a significant increase in the studies on analysisof human faces. Through automatic analysis of faces, it is possible to know the gender, emotional state, and even theidentity of people from an image. Of them, identity or face recognition has became the most important task whichhas been studied for a long time now as it is crucial to take measurements for public security, credit card verification,criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies ongenetic programming algorithm to evolve a binary- and multilabel image classifier …
Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem
Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem
Turkish Journal of Electrical Engineering and Computer Sciences
Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze …
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In the nearest-convex-model type classifiers, each class in the training set is approximated with a convexclass model, and a test sample is assigned to a class based on the shortest distance from the test sample to these classmodels. In this paper, we propose new methods for approximating the distances from test samples to the convex regionsspanned by training samples of classes. To this end, we approximate each class region with a polyhedral convex conicregion by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs. Then, we derive the necessary formulations for computing the distances from test samples to these …
A User Study Of A Wearable System To Enhance Bystanders’ Facial Privacy, Alfredo J. Perez, Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, Jaouad A. Mouloud
A User Study Of A Wearable System To Enhance Bystanders’ Facial Privacy, Alfredo J. Perez, Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, Jaouad A. Mouloud
Information Science Faculty Publications
The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called …
Embedded Face Recognition Combined Mutual Information With Log-Gabor Feature, Jihua Ye, Qingping Lan, Changhong Liu, Shimin Wang
Embedded Face Recognition Combined Mutual Information With Log-Gabor Feature, Jihua Ye, Qingping Lan, Changhong Liu, Shimin Wang
Journal of System Simulation
Abstract: Log-Gabor functions have extended tails at the high frequency end, can effectively improve the shortcoming of ordinary Gabor functions which would over-represent the low frequency components and under-represent the high frequency components, and Log-Gabor filter has no DC components, the bandwidth is not limited, so Log-Gabor filter is more suitable than Gabor filter to extract the face feature. After extracting features of a face image by a Log-Gabor filter bank which is composition of four scales and six orientations, the amount of data is 24 times of the original, but the embedded equipment resources are limited, it is difficult …
Face Recognition Algorithm Based On Mixed Multiple Distance Image And Linear Discriminant Analysis, Yaling Cheng, Aiping Tan, Zhang Min
Face Recognition Algorithm Based On Mixed Multiple Distance Image And Linear Discriminant Analysis, Yaling Cheng, Aiping Tan, Zhang Min
Journal of System Simulation
Abstract: The existing face recognition algorithms used in intelligent monitoring system are mainly applied to short distance images, which still have the problem of low face recognition rate when being applied to long distance images because the image quality decreases as the distance grows. To improve the face recognition accuracy in long distance images, a novel face recognition algorithm was proposed based on using mixed multiple different distance images and Linear Discriminant Analysis (LDA). The proposed algorithm used mixed images extracted from multiple different distances to train images, and used bilinear interpolation method to normalize the image set, and then …
Sparse Representation Based Private Face Recognition In The Cloud, Liu Yan, Jin Xin, Zhao Geng, Xiaodong Li, Yingya Chen, Guo Kui
Sparse Representation Based Private Face Recognition In The Cloud, Liu Yan, Jin Xin, Zhao Geng, Xiaodong Li, Yingya Chen, Guo Kui
Journal of System Simulation
Abstract: In order to protect user privacy, identification computing of user facial image data in the cloud was accomplished, the sparse representation based private face recognition method in the cloud was proposed. Terminal users collected face images and compared with face images in cloud database and then determined whether faces the terminal acquired belonged to the cloud face database, but both could not achieve each other's face image content. The method processed face images in database and cloud into sparse representation via a third-party terminal, then using Paillier and oblivious transfer homomorphic encryption algorithm to contrast sparse representation coefficient vector …
Face Recognition Method Based On Cost-Sensitive Supervised Manifold Learning, Yeqin Cui, Jianguo Gao
Face Recognition Method Based On Cost-Sensitive Supervised Manifold Learning, Yeqin Cui, Jianguo Gao
Journal of System Simulation
Abstract: Existing subspace learning-based face recognition methods assume the same loss from all misclassifications. In the real-world face recognition applications, however, different misclassifications can lead to different losses. Motivated by this concern, a cost-sensitive supervised manifold learning approach for face recognition was proposed. The proposed approach incorporated a cost matrix to specify the different costs associated with misclassifications of subjects, into locality preserving projection algorithm, which devised the corresponding cost-sensitive methods, namely, cost-sensitive locality preserving projections (Cos-Sen LPP), to achieve a minimal overall loss. Three face databases were put into the experiments and experimental results show that Cos-Sen LPP method …
Research On Face Recognition Method Under Uncontrolled Illumination Variation, Kong Rui, Zhang Bing
Research On Face Recognition Method Under Uncontrolled Illumination Variation, Kong Rui, Zhang Bing
Journal of System Simulation
Abstract: A new face recognition algorithm is proposed with high recognition rate under uncontrolled illumination conditions. The new algorithm process face images in advance using Weber local descriptor, which means that the processed image is insensitive to illumination changing. An improved linear discriminant analysis algorithm is adopt for feature extracting, finally, nearest neighbor classifier based on Euclidean distance is applied to classify. The new algorithm is tested on Yale and The Extended Yale Database B face database respectively, in comparison with classic face recognition algorithms, the performance of the proposed method is superior to other's under uncontrolled illumination variation …
Color Face Image Recognition Based On Lbpt Method, Jihua Ye, Yahui Chen, Shimin Wang
Color Face Image Recognition Based On Lbpt Method, Jihua Ye, Yahui Chen, Shimin Wang
Journal of System Simulation
Abstract: Aiming to the shortcomings of exiting algorithm to obtain better color face image information for color facial image recognition, the LBPT algorithm was proposed to realize the high efficiency recognition of color face image. LBPT algorithm reflected the texture features of gray image through adaptively obtaining neighborhood radius, ascertaining the relationship between neighborhood radius and neighborhood pixel number, setting threshold. The RGB color model was used to separate the color face image into the R,G,B three component diagrams. The LBPT algorithm was used to obtain the feature of the component diagrams. In order to realize further recognition, the method …
Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith
Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith
Information Science Faculty Publications
Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the …
Sample Group And Misplaced Atom Dictionary Learning For Face Recognition, Meng Wang, Zhengping Hu, Zhe Sun, Mei Zhu, Mei Sun
Sample Group And Misplaced Atom Dictionary Learning For Face Recognition, Meng Wang, Zhengping Hu, Zhe Sun, Mei Zhu, Mei Sun
Turkish Journal of Electrical Engineering and Computer Sciences
Latest research results have demonstrated the effectiveness of both sparse (or collaborative) representation and dictionary learning for problem solving in face recognition and other signal classification. Considering the fact that an informative dictionary helps a lot in sparse coding, a novel model that consists of group dictionary learning and high-quality joint kernel collaborative representation was proposed in this paper, where rich information from original and virtual space was mined and constructed as a sample group space to improve classification accuracy. Meanwhile, joint kernel collaborative representation with an $\ell_{2}$-regularization-based classifier was used to capture more nonlinear structure and minimize the time …
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
Turkish Journal of Electrical Engineering and Computer Sciences
Face recognition is an essential issue in modern-day applications since it can be used in many areas for several purposes. Many methods have been proposed for face recognition. It is a difficult task since variations in lighting, instantaneous mimic varieties, posing angles, and scaling differences can drastically change the appearance of the face. To suppress these complications, effective feature extraction and proper alignment of face images gain as much importance as the recognition method choice. In this paper, we provide an extensive comparison of the state-of-the-art face recognition methods with the most well-known techniques used in feature representation. In order …
Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.)
Fusion Of Visual And Thermal Images Using Genetic Algorithms, Sertan Erkanli, Jiang Li, Ender Oguslu, Shangce Gao (Ed.)
Electrical & Computer Engineering Faculty Publications
No abstract provided.
Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal
Learning Local Features Using Boosted Trees For Face Recognition, Rajkiran Gottumukkal
Electrical & Computer Engineering Theses & Dissertations
Face recognition is fundamental to a number of significant applications that include but not limited to video surveillance and content based image retrieval. Some of the challenges which make this task difficult are variations in faces due to changes in pose, illumination and deformation. This dissertation proposes a face recognition system to overcome these difficulties. We propose methods for different stages of face recognition which will make the system more robust to these variations. We propose a novel method to perform skin segmentation which is fast and able to perform well under different illumination conditions. We also propose a method …
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that …
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new algorithm using 2DPCA and Gram-Schmidt Orthogonalization Procedure for recognition of face images is proposed. The algorithm consists of two parts. In the first part, a common feature matrix is obtained; and in the second part, the dimension of the common feature matrix is reduced. Resulting common feature matrix with reduced dimension is used for face recognition. Column and row covariance matrices are obtained by applying 2DPCA on the column and row vectors of images, respectively. The algorithm then applies eigenvalue-eigenvector decomposition to each of these two covariance matrices. Total scatter maximization is achieved taking the …
2d Face Database Diversification Based On 3d Face Modeling, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Manuel Filipe Costa (Ed.)
2d Face Database Diversification Based On 3d Face Modeling, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Manuel Filipe Costa (Ed.)
Electrical & Computer Engineering Faculty Publications
Pose and illumination are identified as major problems in 2D face recognition (FR). It has been theoretically proven that the more diversified instances in the training phase, the more accurate and adaptable the FR system appears to be. Based on this common awareness, researchers have developed a large number of photographic face databases to meet the demand for data training purposes. In this paper, we propose a novel scheme for 2D face database diversification based on 3D face modeling and computer graphics techniques, which supplies augmented variances of pose and illumination. Based on the existing samples from identical individuals of …
3d Face Reconstruction From Limited Images Based On Differential Evolution, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Andrew G. Tescher (Ed.)
3d Face Reconstruction From Limited Images Based On Differential Evolution, Qun Wang, Jiang Li, Vijayan K. Asari, Mohammad A. Karim, Andrew G. Tescher (Ed.)
Electrical & Computer Engineering Faculty Publications
3D face modeling has been one of the greatest challenges for researchers in computer graphics for many years. Various methods have been used to model the shape and texture of faces under varying illumination and pose conditions from a single given image. In this paper, we propose a novel method for the 3D face synthesis and reconstruction by using a simple and efficient global optimizer. A 3D-2D matching algorithm which employs the integration of the 3D morphable model (3DMM) and the differential evolution (DE) algorithm is addressed. In 3DMM, the estimation process of fitting shape and texture information into 2D …
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
Electrical & Computer Engineering Theses & Dissertations
A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature …
Neighborhood Defined Feature Selection Strategy For Improved Face Recognition In Different Sensor Modalitie, Satyanadh Gundimada
Neighborhood Defined Feature Selection Strategy For Improved Face Recognition In Different Sensor Modalitie, Satyanadh Gundimada
Electrical & Computer Engineering Theses & Dissertations
A novel feature selection strategy for improved face recognition in images with variations due to illumination conditions, facial expressions, and partial occlusions is presented in this dissertation. A hybrid face recognition system that uses feature maps of phase congruency and modular kernel spaces is developed. Phase congruency provides a measure that is independent of the overall magnitude of a signal, making it invariant to variations in image illumination and contrast. A novel modular kernel spaces approach is developed and implemented on the phase congruency feature maps. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training …
Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam
Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam
Electrical & Computer Engineering Theses & Dissertations
This dissertation focuses on different aspects of face image analysis for accurate face recognition under low resolution and poor lighting conditions. A novel resolution enhancement technique is proposed for enhancing a low resolution face image into a high resolution image for better visualization and improved feature extraction, especially in a video surveillance environment. This method performs kernel regression and component feature learning in local neighborhood of the face images. It uses directional Fourier phase feature component to adaptively lean the regression kernel based on local covariance to estimate the high resolution image. For each patch in the neighborhood, four directional …