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A principal components analysis (PCA) algorithm is one of the most important algorithms that has been used for doing many tasks; for example, data dimension reduction, data compression such as image compression, pattern recognition such as face detection and recognition, and many other things. An improved principal components analysis (IPCA) algorithm is similar to the PCA algorithm except that it...
Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to...
A face recognition method based on improved principal components analysis (PCA) reconstruction is proposed. Firstly, PCA algorithm was performed on training samples of each pattern class to calculate the optimal projection transformation matrices. A point that should be mentioned was that we used median vector rather than mean vector in total scatter matrix. The feature vectors of testing sample could...
In this paper we present a new robust method for recognizing face images using a robust tensorial representation of binary gaussian jet maps (Tensor-Jet). This tensorial representation captures local appearance while retaining information about the spatial structure. During the tensors construction, each Gaussian Jet map is calculated with a Half Octave Gaussian Pyramid using a linear complexity algorithm...
The purpose of this project is to develop a method of providing a complete set of movement commands to an upper extremity neuroprosthesis when the number of command signals available is fewer than required by the mechanical system. The functional relevance of the developed command interface will be tested in a virtual reality environment, where users-able bodied and paralyzed-will use a limited number...
In this paper, we propose a new method for facial expression recognition. We utilize the Candide facial grid and apply principal components analysis (PCA) to find the two eigenvectors of the model vertices. These eigenvectors along with the barycenter of the vertices are used to define a new coordinate system where vertices are mapped. Support vector machines (SVMs) are then used for the facial expression...
Security issues seem to be one of the most important problems of contemporary computer science. One of the most important branches of security is identification of users. In this paper we present the basics of using ear biometrics for person identification. For this purpose we use statistical technique Principal Components Analysis (PCA).
In IPO market, underwriters play an important role as information producer and intermediaries of quality authentication, but underwriters also face creditability problem. Underwriters' reputation is the very index for evaluating the degree of underwriters' creditability, and the measurement on underwriters' reputation is a very important and complex problem, however, mostly in the previous researches...
This paper describes an image segmentation and normalization technique using 3D point distribution model and its counterpart in 2D space. This segmentation is efficient to work for holistic image recognition algorithm. The results have been tested with face recognition application using Cohn Kanade facial expressions database (CKFED). The approach follows by fitting a model to face image and registering...
This paper describes an efficient approach for face recognition as a two step process: (1) segmenting the face region from an image by using an appearance based model, (2) using eigenfaces for person identification for segmented face region. The efficiency lies not only in generation of appearance models which uses the explicit approach for shape and texture but also the combined use of the aforementioned...
Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and principal components analysis(PCA) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which...
In this paper, we propose a method to recognize faces from a set of consecutive video frames instead of a single image using super-resolution (SR). The SR process uses multiple frames acquired from video and combines information coming from them into a single image in higher resolution. As expected, a single low resolution image would contain less amount of information, than the same image taken from...
Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is...
This paper proposes a robust faces recognition method based on the phase spectrum features of the local normalized image. The principal components analysis (PCA) and the support vector machine (SVM) are used in the classification stage. We evaluate how the proposed method is robust to illumination, occlusion and expressions using "AR face database", which includes the face images of 109...
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