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In order to find a set of optimal discriminant vectors which can maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection, a new algorithm of orthogonal optimal discriminant vectors and a new algorithm of statistically uncorrelated optimal discriminant vectors for feature extraction were proposed. Compared with the original MMC feature extraction...
In this paper, a novel supervised feature extraction method called Sub-pattern based Maximum Margin Criterion (SpMMC) is developed for face Recognition. Unlike Maximum Margin Criterion (MMC) method which directly extracts the global features from the whole face image, the proposed SpMMC method separately extracts the local features from the sub-images partitioned from the original face image. Moreover,...
Sparse representation based classification (SRC) has been introduced as a new algorithm for face recognition classification instead of the classical gradient-based algorithms. However, there are some limitations that influence the robustness properties in SRC. One of the most effective parameters that impacts the SRC performance is the directory of training samples. It should contain enough samples...
Face recognition is an active and challenging task in pattern recognition and computer vision application. Sparse representation based classification has been verified to be powerful for face recognition. This paper proposes the metaface block sparse bayesian learning (MBSBL) based on the framework of sparse representation. The MBS-BL combines the metaface learning and block sparse bayesian learning...
This paper explores the viability of Hartley Transforms as an alternative to Fourier Transforms for Face Recognition. The paper provides a brief introduction to Hartley Transform, which is a reasonable alternate to Fourier Transform due to its similarities in the choice of basis function. Correlation filter is a pattern recognition tool that is efficient and robust. This includes extraction of features...
The paper presents two systems to recognize five facial expressions (anger, surprise, joy, sadness and neutral) and gives a performance review on them. Both systems are developed on the same facial features extraction process which is histograms of oriented gradients extraction. Vectors of facial features are classified by the systems using the following proposed methods: template matching method...
In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors...
In this paper, a novel theoretical model of data reduction and multivariate analysis is proposed. The Two-dimensional Factor Analysis is an extension of classical factor analysis in which the images are treated as matrices instead of being converted to unidimensional vectors. By maximally representing the correlation among the pixels, it is able to capture meaningful information about the spatial...
Spatial-multiplexing cameras have emerged as a promising alternative to classical imaging devices, often enabling acquisition of ‘more for less’. One popular architecture for spatial multiplexing is the single-pixel camera (SPC), which acquires coded measurements of the scene with pseudo-random spatial masks. Significant theoretical developments over the past few years provide a means for reconstruction...
The poor alignment and large variations in the temporal sale of facial expressions are two crucial problems for facial expression recognition (FER). Canonical correlation (CC) has recently received increasing attention in surveillance face recognition because of its robustness to variations of alignment. But it could not suit well to FER, because the facial expression variations and temporal information...
In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination...
Face recognition is among the most challenging areas of research in Computer Vision and Pattern Recognition. The early approaches, based on iconic representations and image filtering techniques, have been recently surpassed by new algorithms based on the extraction of distinctive features, and the adoption of powerful descriptors capturing essential information of the face. In this paper several issues...
Facial occlusions such as eyeglasses, hairs and beards decrease the performance of face recognition algorithms. To improve the performance of face recognition algorithms, this paper proposes a novel framework of face recognition combined with the occluded-region detection method. In this paper, we detect occluded regions using Fast-Weighted Principal Component Analysis (FW-PCA) and use the occluded...
Face recognition in videos has been an active topic in the field of object recognition and computer vision. In this paper we propose an automatic face recognition algorithm from video sequences using a template based cross correlation (TBCC) method. It utilizes random selection of frames to form the training template for the discriminant feature representation of a face. The proposed method was tested...
A new shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for invariant face recognition while accommodating expression and illumination variations. The enhanced local binary pattern operator is utilized in the preprocessing stage for facial feature extraction. A phase-shifted and phase-encoded fringe-adjusted joint transform correlator (FJTC) is implemented...
Automatic recognition of suspects from forensic sketches is of considerable interest to the law enforcement agencies. However, this task is complex due to the heterogenous nature of face sketches and photographs. To address this challenge, previous approaches generally learn a transformation of a sketch to photo or a photo to sketch at the image or feature level in order to reduce the modality gap...
Keystroke dynamics is a behavioral biometric modality which uses typing patterns on a keyboard to recognize individuals. The way of typing the password slightly changes with time, because of various factors (including user's training). This modification in the way of typing results in a decrease of performance recognition over time. In this paper, we analyse the correlation between the comparison...
Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i...
The discriminative common vectors (DCV) algorithm shows better face recognition effects than some commonly used linear discriminant algorithms, which uses the subspace methods and the Gram-Schmidt orthogonalization (GSO) procedure to obtain the DCV. However, the Gram-Schmidt technique may produce a set of vectors which is far from orthogonal so that sometimes the orthogonality may be lost completely...
Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective...
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