Vector projection for face recognition

In this paper, we propose a novel face recognition method by using vector projection, which uses vector projection length to evaluate the similarity of two image vectors in face image vector space. The projection length of a test image vector on direction of a training image vector can measure the similarity of the two images. But the decision cannot be made by only a training image which is the most similar to the test one. The mean image vector of each class also contributes to the final classification. Thus, the decision of the proposed vector projection classification (VPC) approach is ruled in favor of the maximum combination projection length. The performance of the proposed VPC approach is evaluated using two standard face databases; a comparative study with the state-of-the-art approaches illustrates the efficacy of the proposed VPC approach.

Robust face recognition based on dynamic rank representation

Face recognition provides a challenging issue in the domain of analyzing images. In this paper a novel approach for face recognition using hybrid SIFT-SVM is proposed. The current database is divided into two parts; training and testing database. The SIFT feature will be created for each training images and the keypoints are computed; then the SVM is applied for the matching process for test images. Results are obtained for three cases child; adult and old age which are made on the basis of age. The recognition rate has been computed by False Acceptance Rate (FAR) and False Rejection Rate (FRR) on these cases and then the results are compared with other algorithms. The recognized result provides robust performance under various conditions like different pose; lighting conditions and facial expressions.

This paper presents a novel approach for face recognition using low cost RGB-D cameras under challenging conditions. In particular, the proposed approach is based on salient points to extract local patches independently to the face pose. The classification is performed using a scalable sparse representation classification by an adaptive and dynamic dictionaries selection. The experimental results proved that the proposed algorithm achieves significant accuracy on three different RGB-D databases and competes with known approaches in the literature.

In this work, we present a novel approach for face recognition which use boosted statistical local Gabor feature based classifiers. Firstly, two Gabor parts, real part and imaginary part, are extracted for each pixel of face images. The two parts are transformed into two kinds of Gabor features, magnitude feature and phase feature. 40 magnitude Gaborfaces and 40 phase Gaborfaces are generated for each face image by convoluting face images with five scales and eight orientations Gabor filters. Then these Gaborfaces are scanned with a sub-window from which the quantified Gabor features histograms are extracted representing efficiently the face image. The multi-class problem of face recognition is transformed into a two-class one of intra-and extra-class classification using intra-personal and extra-personal images, as in [5]. The intra/extra features are constructed based on these histograms of two different face images with Chi square statistic as dissimilarity measure. A strong classifier is learned using boosting examples, similar to the way in face detection framework [10]. Experiments on FERET database show good results comparable to the best one reported in literature [6].KeywordsFace RecognitionIndependent Component AnalysisFace ImageIndependent Component AnalysisConvolutional Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The recent technology of image processing forms the basic principles of research entitled “A Novel Approach for Face Recognition and Age estimation using Local Binary Pattern, Discriminative approach using Two layered Back Propagation Network” has been developed to overcome the inconveniences faced by the organizations in recognizing the exact person. The proposed system sustains a high recognition rate in a wide range of resolution levels and it breaks the other alternative methods. Skin patches are also one of the features of our proposed work. We propose a face detection algorithm for different lighting conditions. Human Skin patches is also one of the parameter in the algorithm. The new methods using Local Binary Pattern, Discriminative approach, facial algorithm and two layered back propagation algorithm for identifying the face and as well as age estimation. The Texture features and Global features are extracted from the image in different scales. The Gradient Orientation Pyramid can be formed for calculating the Age Progression and Age Estimation. The proposed method having high calculation speed compared with the existing method using Back propagation network with single layer. The dataset are taken from FG-NET and Morph Dataset. The performance comparison has been done using different datasets.

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible.

A set-to-set nearest neighbor approach for robust and efficient face recognition with image sets

A C T Face recognition is a pivotal area of research, with various methodologies utilizing different types of information to improve recognition rates.This paper presents a novel approach for face recognition using Multi-Resolution Multi-Threshold Local Binary Patterns (MRMT-LBP) that emphasizes texture information extracted from grayscale images compared to color-based techniques.We propose a systematic generation of multiple threshold LBP representations with four distinct resolutions, resulting in a total of seventeen LBP layers, each corresponding to a different resolution.These layers are then employed to train a face recognition model .The model is constructed by first identifying the LBP layer that achieves the highest recognition rate, which serves as the first channel of our model.Subsequently, additional LBP layers are systematically integrated to form a second channel, with the best complementary layer selected based on its contribution to recognition performance.This iterative process continues until a decline in the recognition rate is observed, at which point the model is built.Comparative evaluations demonstrate that our approach not only achieves superior recognition rates compared to existing grayscale-based face recognition methods but also outperforms prominent color image-based techniques such as RGB and MCF color models.The results underscore the significance of leveraging grayscale images, revealing that the rich texture information they hold can be effectively enhanced to improve face recognition accuracy.

Face recognition is a pivotal area of research, with various methodologies utilizing different types of information to improve recognition rates. This paper presents a novel approach for face recognition using Multi-Resolution Multi-Threshold Local Binary Patterns (MRMT-LBP) that emphasizes texture information extracted from grayscale images compared to color-based techniques. We propose a systematic generation of multiple threshold LBP representations with four distinct resolutions, resulting in a total of seventeen LBP layers, each corresponding to a different resolution. These layers are then employed to train a face recognition model. The model is constructed by first identifying the LBP layer that achieves the highest recognition rate, which serves as the first channel of our model. Subsequently, additional LBP layers are systematically integrated to form a second channel, with the best complementary layer selected based on its contribution to recognition performance. This iterative process continues until a decline in the recognition rate is observed, at which point the model is built. Comparative evaluations demonstrate that our approach not only achieves superior recognition rates compared to existing grayscale-based face recognition methods but also outperforms prominent color image-based techniques such as RGB and MCF color models. The results underscore the significance of leveraging grayscale images, revealing that the rich texture information they hold can be effectively enhanced to improve face recognition accuracy.

Facial image analysis is very useful in many applications such as video compression, talking heads, or biometrics. During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. Face is fairly easy to use and well accepted by people but generally not robust enough to be used in most practical security applications because too sensitive to variations in pose and illumination. One possibility to overcome this limitation is to work in 3-D instead of 2-D. But 3-D is costly and more difficult to manipulate and then ineffective to authenticate people in most contexts. Hence, to solve this problem, we propose a novel face recognition approach that is based on an asymmetric protocol: enrolment in 3-D but identification performed from 2-D images. So that, the goal is to make more robust face recognition while keeping the system practical. To make this 3-D/2-D approach possible, we introduce geometric invariants used in computer vision within the context of face recognition. We report preliminary experiments to evaluate robustness of invariants according to pose variations and to the accuracy of detection of facial feature points. Preliminary results obtained in terms of identification rate are encouraging.KeywordsControl PointFace RecognitionCross RatioGeometric InvariantFacial Feature PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper introduces a novel approach for face recognition using multiple face patterns obtained in various views for robot vision. A face pattern may change dramatically due to changes in the relation between the positions of a robot, a subject and light sources. As a robot is not generally able to ascertain such changes by itself, face recognition in robot vision must be robust against variations caused by the changes. Conventional methods using a single face pattern are not capable of dealing with the variations of face pattern. In order to overcome the problem, we have developed a face recognition method based on the constrained mutual subspace method (CMSM) using multi-viewpoint face patterns attributable to the movement of a robot or a subject. The effectiveness of our method for robot vision is demonstrated by means of a preliminary experiment.

In this paper, we propose a novel approach for face recognition, that combine Supervised Locality Preserving Projection (SLPP) with Maximum Margin Criterion (MMC) for preserving the within-class neighborhood structure of facial manifold and meanwhile finding an optimal feature space for classification. We also give an effective solution to the eigenvalue problem. Our method can avoid the preprocessing stage of resizing the original image resolution and Principle Component Analysis (PCA) projection, so there is no information lost. Experiment results demonstrate the effectiveness of the proposed approach on the ORL face database.

Variation in illumination has a drastic effect on the appearance of a face image. This may hinder the automatic face recognition process. This paper presents a novel approach for face recognition under varying lighting conditions. The proposed algorithm uses adaptive retina modeling based illumination normalization. In the proposed approach, retina modeling is employed along with histogram remapping following normal distribution. Retina modeling is an approach that combines two adaptive nonlinear equations and a difference of Gaussians filter. Two databases: extended Yale B database and CMU PIE database are used to verify the proposed algorithm. For face recognition Gabor Kernel Fisher Analysis method is used. Experimental results show that the recognition rate for the face images with different illumination conditions has improved by the proposed approach. Average recognition rate for Extended Yale B database is 99.16%. Whereas, the recognition rate for CMU-PIE database is 99.64%.

A novel approach for face recognition via domain adaptation and manifold distance metric learning is presented in this paper. Recently, unconstrained face recognition is becoming a research hot in computer vision. For the non-independent and identically distributed data set, the maximum mean discrepancy algorithm in domain adaption learning is used to represent the difference between the training set and the test set. At the same time, assume that the same type of face data are distributed on the same manifold and the different types of face data are distributed on different manifolds, the face image set is used to model multiple manifolds and the distance between affine hulls is used to represent the distance between manifolds. At last, a projection matrix will be explored by maximizing the distance between manifolds and minimizing the difference between the training set and test set. A large number of experimental results on different face data sets show the efficiency of the proposed method.