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Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper,...
This study analyzes the effectiveness of the global (the whole face) and local (regions of eyes, nose, and mouth) features for face recognition. Features describing human faces are encoded in local ternary patterns. The two-class support vector machine is used as the supervised learning algorithm for training recognition models. In the recognition process, recognition modes based on the global features...
This research presents framework for real time face recognition and face emotion detection system based on facial features and their actions. The key elements of Face are considered for prediction of face emotions and the user. The variations in each facial feature are used to determine the different emotions of face. Machine learning algorithms are used for recognition and classification of different...
A novel and complete framework for face recognition with pose variations using only one image is proposed in this paper. Firstly, feature points on face images are located with view-based AAM (Active Appearance Model), based on which, alignment and normalization are operated on face images. Secondly, mapping from non-frontal images to frontal images is constructed based on the algorithm of linear...
Using Kinect acquired RGB-D image to obtain a face feature parameters and three-dimensional coordinates of the characteristic parameters, and to select the characteristic parameter Facial by Candide-3 model, and feature extraction and normalization. Smile face expression data collection through Kinect, SVM collected to smiley face data classify and output the result of recognition, and the results...
Face recognition is an important embodiment of human-computer interaction, which has been widely used in access control system, monitoring system and identity verification. However, since face images vary with expressions, ages, as well as poses of people and illumination conditions, the face images of the same sample might be different, which makes face recognition difficult. There are two main requirements...
A person's face provides a lot of information such as age, gender and identity. Faces allow humans to estimate/ classify the age of other persons just by looking at their face. Researchers who carried out work in studying the process of age classification by humans conclude that humans are not so accurate in age classification; hence the possibility of developing facial age classification methods...
An in-depth study of the multi-classification SVM is given, and the expression classification method based on SVM is proposed for the defects of the traditional classification methods. It realizes fast classification with a relatively small sub-classifier combination, reducing the classification error. Experiments show that the multi-classification method based on SVM can obviously reduce the training...
Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of...
This paper presents theoretically simple, yet computationally efficient approach for face recognition. In this approach the face image is divided into several sub-regions from which the information derived using the Local Binary Pattern (LBP) over a window and the information at the central pixel, which is a product of information source and fuzzy membership value. The LBP features possess the texture...
Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features...
One of the most significant practical challenges for face recognition is a likeness of faces which leads to a big problem in classification of different classes. To tackle this problem, we present a novel method based on similarity of each face with other faces using the Pearson correlation coefficients. Besides, another problem is variability in lighting intensity which its physics are difficult...
This paper describes a method for recognition of continuous facial expression change in video sequences. ASM automatically localizes the facial feature points in the first frame and then tracks the feature points through the video frames. After that the step is the selection of the 20 optimal key facial points, those which change the most with changes in expression. Since the distance of geometric...
A face recognition system uses face to verify individuals using computing capability. However, its performances often degrade due to high dimensional data and large feature appearance of the face image. This paper present a face recognition system based on non linear feature extraction technique to reduce the dimensionality of the face image, called Locally Linear Embedding. This method considers...
The paper presents a novel approach for solving face recognition problem. We combine Gabor filters and Principal Component Analysis (PCA) to extract feature vectors; then we apply Support Vector Machine (SVM), the most powerful discriminative method, and AdaBoost, a meta-algorithm, for classification. Experiments for the proposed method have been conducted on two public face database AT&T and...
Face recognition is an important research field of pattern recognition.Up to now,it caused researchers great concern from these fields,such as pattern recognition,computer vision,and physiology,and so on.Various recognition algorithms have been proposed. Generally,we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify...
In this paper we present a robust and accurate method to detect 17 facial landmarks in expressive face images. We introduce a new multi-resolution framework based on the recent multiple kernel algorithm. Low resolution patches carry the global information of the face and give a coarse but robust detection of the desired landmark. High resolution patches, using local details, refine this location....
Support Vector Machine (SVM) is an effective classifier for classification task, but a vital shortcoming of SVM is that it needs huge computation for large scale learning tasks. Sample selection is a feasible strategy to overcome the problem. In order to rapidly reduce training samples without sacrificing recognition accuracy, this paper presents a novel sample selection strategy based on subspace...
The Support Vector Machine (SVM) is an effective classification tool. Though extremely effective, SVMs are not a panacea. SVM training and testing is computationally expensive. Also, tuning the kernel parameters is a complicated procedure. On the other hand, the Nearest Neighbor (KNN) classifier is computationally efficient. In order to achieve the classification efficiency of an SVM and the computational...
The similarity of human faces, unpredictable variations and aging are the crucial obstacles in face recognition. To handle this if large set of training images are used then computational complexity will get increase as images are rather high dimension but if training set kept small, performance decreases. Since both classification and feature information are necessary for a recognition system DCT...
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