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In this paper, a novel supervised locality preserving projection method based on differential mode is proposed. We proposed the concept of within-class separation degree and between-class separation degree. The discriminant criterion of difference mode based on within-class separation degree matrix and between-class separation degree matrix is constructed. Thus, the singular problem of within-class...
Blurred face restoration and recognition are closely related problems. Lots of existing works have proven that these two tasks contribute to each other, but only a few consider combining them together. In this paper, we propose Simultaneous Restoration and Recognition (SRR) method by iteratively dealing with these two tasks. Two new models are presented and then integrated into a two-staged method...
Popularity of surveillance and mobile cameras provides great opportunities to video-based face recognition (VFR) in less-controlled conditions. This paper proposes a joint space learning method to simultaneously identify the most representative samples and discriminative features from facial videos for reliable face recognition. Specifically, we use a mixture modal by learning multiple feature spaces...
This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate...
Face recognition for biometric purposes has an advantage of being a non-contact process. Various face recognition algorithms has been proposed in the literature. The face recognition system mainly consists of two steps i.e. feature extraction / reduction and classification. One of the most popular tool, Principal Component Analysis (PCA) is used for feature extraction. For classification purpose,...
Facial pose grouping plays an important role in the video face recognition. In this paper, we present an unsupervised facial pose grouping approach via Garbor subspace affinity and self-tuning spectral clustering. First, we utilize the local normalization method to reduce the impact of uneven illuminations, and then extract the discriminative appearance features via Gabor wavelet representation. Next,...
The important objective of this work is to utilization of entire Gabor features by enhancing the phase part of the Gabor and maximizing the Fishers ratio in nonlinear domain space by preserving the local information. Entire Gabor kernel locality preserving Fisher discriminant analysis (EGKLPFDA) approach is proposed. Both Gabor magnitude and spatially enhanced phase congruency parts are separately...
Recognition under uncontrolled lighting conditions remains one of the major challenge for practical face recognition systems. In this work, we present an efficient and effective framework to improve the recognition performance from two aspects: image preprocessing and subspace representation. The step of image preprocessing is mainly used to eliminate the effects of illumination. The step of subspace...
In this paper, we propose a new face image restoration method based on the features extracted from the noisy images given by the principal components of the noise covariance matrix. This technique deliberate the additive normal scattered degradation classic and use of a code shrinkage technique to remove noise from the images. The proposed work have been a large number of artificial neural networks...
This paper presents a comparison between one-dimensional component analysis (1D-PCA) and two-dimensional principal component analysis (2D-PCA) under two different types of classification techniques namely k-nearest neighbor (kNN) and Support Vector Machines (SVM). These two techniques differ in the method to determine the image covariance matrix. 2DPCA used 2D image matrices instead of column vectors...
In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems,...
Analyzing social relations through image processing is an active and emerging research topic. Family is the basic unit of a society; recognizing and categorizing family photos is an essential step towards image-based social analysis. In this paper, we propose an approach that leverages a geometric model and an appearance model for family photo detection. The geometric feature captures scene-level...
Many modern face verification algorithms use a small set of reference templates to save memory and computational resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well-principled approach, named sparse support faces, that can outperform state-of-the-art methods both in terms of recognition...
In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation determination. Compared to the standard approach employing the class mean vectors for class representation,...
In this paper a framework is presented to deals with various aspects of face recognition like illumination, rotation and scaling. The proposed framework consists of three parts. In the first part Gabor filter is used over the thermal faces at different scales, locations, and orientations. In second part, the fixed point algorithm Fast ICA have been used over the Gabor filtered images to represent...
For which low frequency discrete cosine transform (DCT) coefficients retransforming based on contrast limiting adaptive histogram equalization (CLAHE) is proposed. Firstly, original images are divided into several non-overlapping blocks and CLAHE is used to do local contrast stretching so as to reduce noise. Then, illustration variation of face image is removed by reducing suit numbers of low frequency...
A comparative study on face recognition based on two methods, SVM of One-against-One method and SVM of One-against-Rest method, has been carried out in this paper. Our method consists of three parts: Firstly, the image robustness on illumination and posture has been improved by the processing of the Gabor wavelet. Secondly, the bilateral 2DLDA technique on image is adopted to realize the dimension...
In this paper, we present a rich image representation which is robust to illumination, facial expression and scale variations. For this aim, firstly, we propose a novel dense local image representation method based on Walsh Hadamard Transform (WHT) called Local WHT (LWHT). LWHT is the application of WHT to each pixel of an image to decompose it into multiple components, called LWHT maps. Secondly,...
Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting...
Biometric based identifications are widely adopted for personnel identification. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition considering multimodal biometric traits can be achieved. This paper introduces the Multimodal Personnel Authentication using Finger vein and Face Images (MPAFFI)...
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