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Holistic approaches of face recognition are not robust to illumination, scale, occlusion and age variations. Various studies indicate that the performance of holistic approaches degrades as the face database size increases. In this paper, we propose a user specific landmark geometry based approach that assigns weights to different geometrical distances according to their role in face recognition process...
The goal of this paper is to present a critical comparison of existing classical techniques on recognition of human faces. This paper describes the four major classical face recognition techniques i.e., i) Principal Component Analysis (PCA), ii) Linear Discriminant Analysis (LDA), iii) Discrete Cosine Transform (DCT), and iv) Independent Component Analysis (ICA). Strong and weak features of these...
Extracting robust and discriminatory features from images is a crucial task for infrared face recognition. For this reason, we have developed an infrared face recognition algorithm based on improved local features, which applies adaptive threshold quantization to encode the local directional energy. The conventional LBP-based feature as represented by the fix threshold encoding has limited distinguishing...
Face recognition in extreme situations is still challenging to researchers. While several algorithms have shown great recognition results in ideal conditions, accuracy decreases when recognition tasks present a high illumination variation. In this paper, we propose to add two components to the recognition system to make the surf descriptor efficient in such extreme situations. First, we learn a discriminant...
Image fusion is widely recognized as an important technique in pattern recognition and computer vision. The object of image fusion is fuse one or more source images with different focus points in to one image, so that the result of image fusion is an image which is more clarity and better information. This paper presents a novel and improved pixel-level multifocal image fusion technique has been implemented...
Biometric information security is a relatively new research interest in the field of biometric authentication. In view of the special relationship of human face and human ear in physiological position, the fusion of face and ear is a natural form of multimodal authentication system used for non-intrusive personal identification. This paper proposes a template protection method for multimodal biometric...
In face recognition, the illumination variation problem in uncontrolled environments has gained some research activities. Although the quotient image based methods are reported to be a simple yet practical technique in face recognition, these methods could not satisfactorily maximize the ratios of between-class and within-class scatter and may not effectively be used for the illumination variation...
In this article we address the issue of adopting a local sparse coding representation (Histogram of Sparse Codes), in a part-based framework for inferring the locations of facial landmarks. The rationale behind this approach is that unsupervised learning of sparse code dictionaries from face data can be an effective approach to cope with such a challenging problem. Results obtained on the CMU Multi-PIE...
Gender classification can play a significant role in security and surveillance system. It aids in identification of a person by recognizing its gender (male/female) from the face image only. Extracting discriminate features for male and female is a fundamental and challenging problem in the field of computer vision. In this manuscript, a combination of Approximation Face Image (AFI) with Principal...
In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods...
This paper shows a work done under Affective Computing umbrella and in the field of emotion recognition. The paper explores the anatomy of a human face and builds the classification model based on it. The anatomical information of face is used to locate several points on the face and to extract the features. The features are in form of distance vectors which can be of specific person or group of persons...
In this paper, the Gabor filter is studied and further expanded for temporal facial expression analysis. Originally, the Gabor feature describes both spatial and frequency characteristics of 2D images. The prominent of the theorem has been validated in research communities for a decade due to its similarity to the human perception system. The performance of the filter in the existing research gives...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
The order polytopes we consider here are the linear order polytope, the interval order polytope, the semiorder polytope and the partial order polytope. Among their known facet defining inequalities (FDIs), many have their coefficients in {−1, 0, 1}. We consider the problem of finding all of these particular FDIs. The problem is easy for the partial order polytope. For the interval order polytope,...
Image set based face recognition provides more opportunities compared to single mug-shot face recognition. However, modelling the variations in an image set is a challenging task. We propose a computationally efficient and accurate image set modelling technique. The idea is to reconstruct each image set sample with an unlabeled dictionary using the computationally efficient regularized least squares...
Face recognition using eigenfaces is a popular technique based on principal component analysis (PCA). However, its performance suffers from the presence of outliers due to occlusions and noise often encountered in unconstrained settings. We address this problem by utilizing L1-eigenfaces for robust face recognition. We introduce an effective approach for L1-eigenfaces based on combining fast computation...
In the present work, appearance-based face recognition method called the Laplacianface approach is used. The face images are mapped into a face subspace for analysis by using Locality Preserving Projections(LPP). The technique is different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space. The main goal of...
The human face is one of the easiest characteristic, which can be used in biometric security system to identify a user. Face recognition technology, is very popular and it is used more widely because it does not require any kind of physical contact between the users and the device. Camera scans the user face and match it to a database for verification. Furthermore, it is easy to install and does not...
Limited size of object images and lack amount of training data would degrade the performance seriously in modern-day recognition applications. Therefore, how to effectively utilize available information from images becomes more and more important. In this paper, we propose to extend the linear regression classification (GLRC), which can effectively use all the information in cases of multiple inputs,...
This paper focuses on the study of modified constructive training algorithm for Multi Layer Perceptron “MLP” which is applied to face recognition applications. In general, constructive learning begins with a minimal structure, and increases the network by adding hidden neurons until a satisfactory solution is found. The contribution of this paper is to increment the output neurons simultaneously with...
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