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An image level fusion technique combines different form of images so that the combine image may contain more relevant information than the individual ones. An image fusion technique for face recognition is presented in this paper. Due to the topological structure of human face images, one can easily identify a face image by looking along the horizontal-vertical and also forward-backward directions...
Image level fusion combines an image in different ways with its original version so that the combine image may contain more relevant information than the original one. This paper presents a novel method for face recognition by fusing original and corresponding diagonal images. Two ways of image fusion technique have been performed here. Firstly, we generate diagonal face image from original face image...
This paper presents a novel scheme for feature extraction for face recognition by fusing local and global discriminant features. The facial changes due to variations of pose, illumination, expression, etc. are often appeared only some regions of the whole face image. Therefore, global features extracted from the whole image fail to cope with these variations. To address these problems, face images...
Two-Dimension Linear Discriminant Analysis (2DLDA) and complex-matrix LDA are two noticeable improvements to conventional LDA. They can achieve a good performance respectively. However, the complex-matrix LDA is very suitable for bimodal biometrics. In this paper, we indicate the two available implementation procedures of complex-matrix, i.e. the original implementation procedure and one alternative...
In this paper, we propose to adopt an entry-wise Hadamard-Schur (HS) product to extract facial information directly from raw face image pixels. With the goal of extracting relevant pixel features from each image, four different HS projection matrices which consist of directional binary patterns are proposed. Subsequently, the match score outputs from these four HS projections are fused via a total...
This paper presents a novel and efficient approach for face recognition based on extended local binary patterns (LBP) and fuzzy information fusion. Each facial image in training set is divided into a certain number of sub-regions after a simple image preprocessing, and all training sub-regions from the same position construct a new training subset. The extended LBP method is used to extract local...
While biometrics-based identification is a key technology in many critical applications such as searching for an identity in a watch list or checking for duplicates in a citizen ID card system, there are many technical challenges in building a solution because the size of the database can be very large (often in 100s of millions) and the intrinsic errors with the underlying biometrics engines. Often...
The objective of this research is to embed topology within the dynamic Bayesian network (DBN) formalism. This extension of a DBN (that encodes statistical or causal relationships) to a topological DBN (TDBN) allows continuous mappings (e.g., topological homeomorphisms), topological relations (e.g., homotopy equivalences) and invariance properties (e.g., surface genus, compactness) to be exploited...
In this paper, we exploit the multi-modal face recognition capability by a comparative study on 6 fusion methods in the score level, which can be divided into 2 kinds: (1) simple fusion without data training, such as Sum, Product, Max and Min; (2) complex fusion including a predefined data training section, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Our experiments...
The effect of different acquisition distances on the performance of face verification is studied. In particular, we evaluate two standard approaches using popular features (DCT and PCA) and matchers (GMM and SVM) under variation in the acquisition distance, as well as their score-level combination. The DCT-GMM-based system is found to be more robust to acquisition distance degradation than the PCA-SVM-based...
In human facial behavioral analysis, Action Unit (AU) coding is a powerful instrument to cope with the diversity of facial expressions. Almost all of the work in the literature for facial action recognition is based on 2D camera images. Given the performance limitations in AU detection with 2D data, 3D facial surface information appears as a viable alternative. 3D systems capture true facial surface...
A wide variety of present approaches work well in detecting frontal faces, but they are often unable to detect faces with partial occlusion, rotation and strong shadows. To address this problem, we propose an efficient technique. First, we filter the face-like regions from the input image using skin-color model. And then component classifiers are used to detect the faces' components from these potential...
In this paper, we propose a novel fusion-based gender classification method for 3D frontal neutral expression facial shape. Face landmarks, extracted from 3D face shape based on profiles and curvature, are separated as four regions. Experimental investigation to evaluate the significance of different facial regions in the task of gender classification is performed. The classification is performed...
Presently, face recognition has two main barriers, which are the variation of illumination, expression, pose and the occlusion and disguise respectively. The problem of robust identification human faces with varying expression and illumination, as well as occlusion and disguise will be researched in this paper. Firstly, singular value decomposition will be used for the face image, casting the singular...
Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face...
The important issue in multi-class classification on support vector machines is the decision rule, which determines whether an input pattern belongs to a predicted class. To enhance the accuracy of multi-class classification, this study proposes a multi-weighted majority voting algorithm of support vector machine (SVM), and applies it to overcome complex facial security application. The proposed algorithm...
In this paper, we propose a new method to enhance the quality of near infrared face image using tensorface, super-resolution and image fusion. Given a single model of near infrared face image which is not suitable for human to recognize or verify and its low-resolution sample, we can synthesize an image under visible light environment by building multiple factors training tensors and super-resolving...
Two-dimensional face recognition suffered from pose changes, while three-dimensional approaches are with high computational complexity. Motivated by this, a two-view face recognition system for digital home is presented in this paper. Besides the improvement in recognition rate, this system reduces the misclassification that could occur in traditional single-view systems. The proposed system fuses...
We present a low resolution face recognition technique based on a special type of convolutional neural network which is trained to extract facial features from face images and project them onto a low-dimensional space. The network is trained to reconstruct a reference image chosen beforehand, and it has been applied in visible and infrared light. Since the learning phase is achieved separately for...
The popularity of biometrics and its widespread use introduces privacy risks. To mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometrics were introduced, also known as the field of template protection. In parallel to these developments, fusion of multiple sources of biometric information have shown to improve the verification performance...
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