Face Recognition using Image Gradient Regression Approach (original) (raw)
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Face Recognition Using Kernel Ridge Regression
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007
In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.
Review of Face Recognition System
2020
Face recognition is one of the most suitable applications of image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces. While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently due to more abundant information than still images. Video-based face recognition has been one of the hot topics in the field of pattern recognition in the last few decades. This paper presents an overview of face recognition scenarios and video-based face recognition system architecture and various approaches are used in video-based face recognition system which can not only discover more space-time semantic information hidden in video face sequence, but also make full use of the high level semantic concepts and the intrinsic nonlinear structure information to extract discriminative manifold features. We also compare our algorithm with other algorithms on our own database.
Facial Recognition System: A Review
2015
Biometric Recognition has always been the chief aspect for identification and verification, facial recognition among these is an increased use due to its authenticity and mass identification properties. Facial Recognition involves particular choice of features where feature selection involves concluding upon unique ones for better classification and simultaneously provides enhanced discriminatory power. PCA [Principal Component Analysis], works on orthogonal projection basis for recognition with Eigen faces of decreased face space, Independent Component Analysis [ICA] searches for linear transformation. PCA+LDA[Linear Discriminant Analysis] is applied continuously to a smaller and smaller set of samples, better separating classes while the number of classes become small deep down the tree. Application of kernel subspace representations to face recognition, gives us better discrimination. Lesser face space corrupts the software. Obstacles such as illumination and expressional varianc...
Face Recognition Systems: A Survey
Sensors
Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, i...
Implementation and Evaluation of Face Recognition Based Identification System
International Journal of Intelligent Systems and Applications in Engineering
Face recognition has been widely used and implemented to many systems for the purpose of authentication, identification, finding faces, etc. In this study Yale face database [1] is used which consist of 15 different people. For each of person there are 11 different images with different face expressions. In this study images are categorized as normal, normal and centre light, normal and happy, normal with left light and right light. In order to recognize these faces 4 different face recognition methods namely Eigenface, Fisherface, LBPHface and SURF are utilized in the developed environment. In order to test the mentioned face recognition algorithms a software is developed using EmguCV in .NET environment. After evaluating and comparing the obtained confusion matrix amongst other the LBPHface method was found to be superior method with an average accuracy of 99%, it was ~98% SURF, ~97% for EigenFace and FisherFace. FicherFace was slightly better then the Eigenface method.
Mathematical Problems in Engineering, 2018
Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF),...
A Review on Face Recognition Algorithms
2017
Face recognition has been challenging and interesting area in real time applications. Face recognition is a form of biometric identification that relies on data acquired from the face of an individual. A large number of face recognition along with their modifications, have been developed during the past decades. Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. In real world applications, it is desirable to have a stand-alone, embedded facerecognition system. The reason is that such systems provide a higher level of robustness,hardware optimization, and ease of integration. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This include PCA, ICA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition, LBP and various hybrid combination ...
Face Recognition Algorithms: A Review
Due to its applicability in different domains of life face recognition is a very fast growing area of research. In daily life, to receive information and interpret it and to identify familiar faces, face recognition is used. It is prevalent due to its simplicity and performance. In the last few years tremendous research has been carried out but still there are many challenges related to face recognition. In covid time it becomes challenging to identify a mask wearing face. This paper aims to provide an overview of some of the well known facial recognition algorithms and techniques used in research. Initially face recognition was implemented using Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machine, Adaboost but nowadays to improve the quality deep learning is used.
Face Recognition using Artificial Intelligent Techniques
AL-Rafidain Journal of Computer Sciences and Mathematics
Face recognition is considered one of the visual tasks which humans can do almost effortlessly while for computers it is a difficult and challenging task. This research deals with the problem of face recognition. A novel approach is presented for both face feature extraction and recognition, first, we introduce Principal Component Analysis (PCA) for face feature extraction, Generalized Regression Artificial Neural network for face recognition. The performance of the whole system was done after training with 120 color images (40 human faces with 3 poses) and testing using 40 color images. The images were taken from Collection of Facial Images: Faces95 by Computer Vision Science Research Projects. Experimental results for proposed human face recognition confirm that the proposed method lends itself to good extraction and classification accuracy relative to existing techniques.