New Method For Face Recognition Research Articles

Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.

Enhanced Face Recognition Using Adaptive Local Tri Weber Pattern with Improved Deep Learning Architecture

Effective face recognition is accomplished using the extraction of features and classification. Though there are multiple techniques for face image recognition, full face recognition in real-time is quite difficult. One of the emerging and promising methods to address this challenge in face recognition is deep learning networks. The inevitable network tool associated with the face recognition method with deep learning systems is convolutional neural networks (CNNs). This research intends to develop a new method for face recognition using adaptive intelligent methods. The main phases of the proposed method are (a) data collection, (b) image pre-processing, (c) normalization, (d) pattern extraction, and (e) recognition. Initially, the images for face recognition are gathered from CPFW, Yale datasets, and the MIT-CBCL dataset. The image pre-processing is performed by the Gaussian filtering method. Further, the normalization of the image will be done, which is a process that alters the range of pixel intensities and can handle the poor contrast due to glare. Then a new descriptor called adaptive local tri Weber pattern (ALTrWP) acts as a pattern extractor. In the recognition phase, the VGG16 architecture with new chick updated-chicken swarm optimization (NSU-CSO) is used. As the modification, VGG16 architecture will be enhanced by this optimization technique. The performance of the developed method is analyzed on two standards face database. Experimental results are compared with different machine learning approaches concerned with noteworthy measures, which demonstrate the efficiency of the considered classifier.

Face Recognition Based on the Coefficient Tree for Three Scale Wavelet Transformation

Abstract—A new method for face recognition is presented in the paper. The new method based on the coefficient tree for three-scale wavelet transformation is presented for solving a problem on feature separation. The hidden Markov model is used for classifying face image features.

A method of face recognition using 3D facial surfaces

Face recognition is one of the most rapidly developing areas of image processing and computer vision. In this work, a new method for face recognition and identification using 3D facial surfaces is proposed. The method is invariant to facial expression and pose variations in the scene. The method uses 3D shape data without color or texture information. The method is based on conformal mapping of original facial surfaces onto a Riemannian manifold, followed by comparison of conformal and isometric invariants computed in this manifold. Computer results are presented using known 3D face databases that contain significant amount of expression and pose variations.

Score Fusion of 3DLBP and ACDN+P for 3D Face Recognition

Many situations of our everyday life require our identification. Biometrics-based methods, besides allowing such identification, can help to prevent frauds. Among several biometrics features, face is one of the most popular due to its intrinsic and important properties, such as universality, acceptability, lowcosts, and covert identification. On the other hand, the traditional automatic face recognition methods based on 2D features can not properly deal with some very frequent challenges, such as occlusion, illumination and pose variations. In this paper we propose a new method for face recognition based on the fusion of 3D low-level local features, ACDN+P and 3DLBP, using depth images captured by cheap Kinect V1 sensors. In order to improve the low quality of the point cloud provided by such devices, Symmetric Filling, Iterative Closest Point, and Savitzky-Golay Filter are used in the preprocessing stage of the proposed method. Experimental results obtained on EURECOM Kinect dataset showed that the proposed method can improve the face recognition rates.

Face Recognition Algorithm Using Two Dimensional Locality Preserving Projection in Discrete Wavelet Domain

This paper proposes a new method for face recognition using Two Dimensional Locality Preserving Projection (2DLPP) in Discrete Wavelet Domain (DWT). Firstly, the dimensionality of the original face image is reduced by using the discrete wavelets transform and low frequency channel (LL) is selected to be as the features of face image. And then 2DLPP is applied to decrease the dimension of LL. Finally, the nearest neighbour classifier is used for face classification. The experiment results on ORL face database demonstrate the effectiveness of the proposed method.

Designing A New Face Recognition System Robust To Various Poses

Different scholars in the world design wide varieties of systems for automatic face recognition process. The face recognition process is dependent on different variables, such as the illumination and the different poses of the image. Therefore, face recognition process is still a fundamental issue in image processing. In this paper, we have developed a new method for face recognition based on ant colony algorithm. To assess the performance and effectiveness of the designed system, face images available in ORL database are used. The results obtained indicate that the proposed method for face recognition accuracy is about 97.3 percent. Besides, comparisons indicate that the performance of the proposed method compared to other methods enjoys a remarkable accuracy.

Augmented Lanczos bidiagonalization by small singular value decompositions for face recognition and image compression

A new method for face recognition and image compression is presented here. In this method, Lanczos bidiagonalization is combined with sequences of small singular value decompositions. The goal of this method is to perform the truncated singular value decomposition for large matrices. It is an extension of standard Lanczos bidiagonalization, which improves its approximation capabilities and keeps the computational cost reasonable. Face recognition and image compression examples show the new method is competitive with pervious methods.

Biview face recognition in the shape–texture domain

Face recognition is one of the biometric identification methods with the highest potential. The existing face recognition algorithms relying on the texture information of face images are affected greatly by the variation of expression, scale and illumination. Whereas the algorithms based on the shape topology weaken the influence of illumination to some extent, but the impact of expression, scale and illumination on face recognition is still unsolved. To this end, we propose a new method for face recognition by integrating texture information with shape information, called biview face recognition algorithm. The texture models are constructed by using subspace learning methods and shape topologies are formed by building graphs for face images. The proposed biview face recognition method is compared with recognition algorithms merely based on texture or shape information. Experimental results of recognizing faces under the variation of illumination, expression and scale demonstrate that the performance of the proposed biview face recognition outperforms texture-based and shape-based algorithms.

Face Recognition Algorithm based on Doubly Truncated Gaussian Mixture Model using DCT Coefficients

In this paper a novel and the new method for face recognition is developed and analyzed using doubly truncated multivariate Gaussian mixture model. The 2D DCT coefficients as the feature vector of the each individual face is modelled by k component mixture of doubly truncated multivariate Gaussian distribution. The number of components and initialization of the model parameter’s are obtained by the k-means algorithm and face image histogram. Using the EM algorithm the model parameter’s are obtained. A face recognition algorithm is developed by a maximum likelihood function under baysian framework. The efficiency of the developed algorithm is evaluated by obtaining the recognition rates using JNTUK face database and YALE database. This algorithm out perform the face recognition algorithm based on GMM with DCT coefficients.

A New Face Recognition Method Based on Fast Least Squares Support Vector Machine

In this paper we propose a new method for face recognition by combining Independent Component Analysis(ICA) and Support Vector Machine(SVM). Firstly we extract face features by using Informax algorithm. We then implement face recognition using FLS-SVM algorithm. We compare classical method of Principal Component Analysis (PCA) and our new method using ORL face database. The experiment results show the performance of our method is significantly superior to that of PCA-based method. The recognition speed of our method is faster than that of classical SVM algorithms.

Face recognition under arbitrary illumination using illuminated exemplars

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.

Do singular values contain adequate information for face recognition?

Singular values (SVs) have been used for face recognition by many researchers. In this paper, we show that the SVs contain little useful information for face recognition and most important information is encoded in the two orthogonal matrices of the SVD. Experimental results are given to support this observation. To overcome this problem, a new method for face recognition based on the above finding is proposed. The face image is projected on to the orthogonal basis of SVD and then the vectors of coefficients are used as the face image features. By using probability density of this image feature obtained by a simplified EM algorithm, the Bayesian classifier is adopted to recognize the unknown faces. The proposed algorithm obtains acceptable experimental results on the ORL face database.