A Method of True and Fake Objects Pattern Recognition Integrating Image Information and Spectral Information
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At present, pattern recognition technology has been widely used in the fields of objects, faces, fingerprints, military target recognition, etc. However, pattern recognition method still has obvious shortcomings when applied to the above fields. It is currently restricted to the use of image information for identification. When the image features of the research object are highly similar, the accuracy of pattern recognition is low and cannot meet the actual application requirements. For example, in the case of mixed true and false targets, it is difficult to obtain satisfactory recognition results using only image information. Aiming at the above problems, a pattern recognition method integrating image information and spectral information is proposed in this paper. Firstly, the image recognition model based on the convolutional neural network model is built to identify object categories based on the semantic features of objects and obtain preliminary recognition results. Then, on the basis of the preliminary recognition results, the measured spectral data of the object (spectrum range 400-1 000 nm, spectral resolution 2 nm) is used to perform true and fake identification of the object based on the back propagation (BP) neural network model. The principle of true and false recognition is that the true and false targets are different in material, causing significant difference in their hyperspectral information. Finally, recognition results are obtained. In order to verify the accuracy of the proposed method, true and fake apples and grapes are used as experimental subjects and the result is that:The recognition accuracy obtained by using only image information is 38.50%, and the recognition accuracy obtained by using only spectral information is 63.00%, however, the recognition accuracy obtained by the method proposed in this paper is 95.00%. Compared with the existing pattern recognition method without spectral information participation, the pattern recognition method using image information and spectral information proposed in this paper improves the pattern recognition accuracy under the mixed condition of true and false targets, and can be widely applied to object recognition, face recognition, fingerprint recognition, military target recognition and other fields.Keywords:
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Face Recognition System is traditional research and development in face detection and tracking are focused on video images or in still images. A face recognition system for classification and authentication using Genetic Algorithm and Optimization Soft Computing Techniques are proposed. The system is framed with three steps. Initially Image pre-processing methods are applied on the input image. Secondly, a neural based algorithm is presented, to detect frontal views of faces. The dimensionality of face image is reduced by the Principal Component Analysis (PCA) and the recognition is done by the Back propagation Neural Network (BPNN). These applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. Thirdly, Gabor feature extraction and feature selection using Genetic Algorithm (GA) is applied in the final step for recognizing the faces. The proposed approaches are tested on a number of face images. Experimental results demonstrate the higher extent performance of these algorithms. Here 200 face images from Yale database are taken and some performance metrics like acceptance ratio and execution time are calculated. Neural based face recognition is better performance of more than 90 % acceptance ratio. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications.
Three-dimensional face recognition
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Face recognition has been an important research direction in computer vision for a long time. The algorithms proposed in related fields are endless, and the accuracy that can be achieved is higher than ever. However, it is still quite difficult to apply face recognition technology. This paper combines the algorithms of face detection and face recognition to build a video-based face recognition system for efficient and accurate recognition of the faces of specified characters in the video. The system extracts the image of the person to be detected through MTCNN, and then uses Facenet to extract its features. Finally, the images are classified by SVM, thereby detecting the person to be detected appearing in the video. Experiments show that our method can still achieve good recognition results when the target is lacking data and the picture quality of the video is unstable. The accuracy of our method in self-built dataset in this paper can reach 94.9%.
Three-dimensional face recognition
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Three-dimensional face recognition
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Face recognition is a hot research topic in the fields of pattern recognition and computer vision, which has been found a widely used in many applications, such as verification of credit card, security access control, and human computer interface. As a result, numerous face recognition algorithms have been proposed, and surveys in this area can be found. Although many approaches for face recognition have been proposed in the past, none of them can overcome the main problem of lighting, pose and orientation. For a real time face recognition system, these constraints are to be a major Analysis (PCA) .These methods challenge which has to be addressed. In this proposed system, a methodology is given for improving the robustness of a face recognition system based on two well-known statistical modelling methods to represent a face image: Principal Component extract the discriminates features from the face. Preprocessing of human face image is done using Gabor wavelets which eliminates the variations due to pose, lighting and features to some extent. PCA extract low dimensional and discriminating feature vectors and these feature vectors were used for classification. The classification stage uses nearest neighbour as classifier. This proposed system will use the YALE face data base with 100 frontal images corresponding to10 different subjects of variable illumination and
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Three-dimensional face recognition
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Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is the prime concern. Face image is most popular non-intrusive and non-invasive biometrics whose image can easily be taken without user co-operation. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges and face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. It also avoids generalizability problem which is caused due to subspace discriminant analysis or statistical learning procedure by using a non-statistical procedure which avoids training step for face samples. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset if feature vectors for actual recognition.
Three-dimensional face recognition
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Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.
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This paper offers a suggested method of facial recognition to identify any person. Initially, captured images in the database to apply matching processing, which ease the recognition operation. The most difficult problems in the recognition algorithms are, the difference in face appearance, the lighting effect, and the composite background of the image. This paper presents the fundamental step in the extraction of the properties that depend on the Principal Component Analysis (PCA) also known as Karhunen-Loe (KL) to convert Eigen values to reduce the number of inputs on the network. To identify a person's image, apply Artificial Neural Networks (ANN) using Elman Neural Network (ENN), which is the most necessary and effective applications suitable for biometric systems and image processing. Practically taking photos (10) each person has eight poses (4 to 4 training to test). The experimental results of the ENN classification are calculated as a true acceptable rate (GAR) of 97% while the false acceptable rate FAR is equal to 3%. In addition, the artificial neural network gave a good performance to anyone.
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Facial recognition has most significant real-life requests like investigation and access control. It is associated through the issue of appropriately verifying face pictures and transmit them person in a database. In a past years face study has been emerging active topic. Most of the face detector techniques could be classified into feature based methods and image based also. Feature based techniques adds low-level analysis, feature analysis, etc. Facial recognition is a system capable of verifying / identifying a human after 3D images. By evaluating selected facial unique features from the image and face dataset. Design from transformation method given vector dimensional illustration of individual face in a prepared set of images, Principle component analysis inclines to search a dimensional sub-space whose normal vector features correspond to the maximum variance direction in the real image space. The PCA algorithm evaluates the feature extraction, data, i.e. Eigen Values and vectors of the scatter matrix. In literature survey, Face recognition is a design recognition mission performed exactly on faces. It can be described as categorizing a facial either "known" or "unknown", after comparing it with deposits known individuals. It is also necessary to need a system that has the capability of knowledge to recognize indefinite faces. Computational representations of facial recognition must statement various difficult issues. After existing work, we study the SIFT structures for the gratitude method. The novel technique is compared with well settled facial recognition methods, name component analysis and eigenvalues and vector. This algorithm is called PCA and ICA (Independent Component Analysis). In research work, we implement the novel approach to detect the face in minimum time and evaluate the better accuracy based on Back Propagation Neural Networks. We design the framework in face recognition using MATLAB 2013a simulation tool. Evaluate the performance parameters, i.e. the FAR (false acceptance rate), FRR (False rejection Rate) and Accuracy and compare the existing performance parameters i.e. accuracy.
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Face recognition is a kind of important method focused on biological information identification, which is also a research hotspot in the field of pattern recognition and machine vision. In recent years, some pattern recognition researches show that, human visual system uses a lot of visual-based deep information. Therefore, for face recognition in complex environment, we have research focus on depth images based face recognition system, in order to overcome the problem that the 2-D face recognition system is so sensitive to pose, facial expression and illumination changes. It is remarkable that when we apply statistical method to solve the problems of face depth images recognition, we extremely design feature extraction algorithm for specific training sample set. Nevertheless, once these feature extraction algorithms is completed, there will never be any improvement among them. Thus, this situation leads to the poor universality of the feature extraction algorithms, and the effectiveness and stability of the algorithm will be significantly decreased. As the result, the performance of the recognition system is finally affected. In this paper, we focus on the universality problem of feature extraction algorithm and system identification performance, combining feedback learning theory with Neural Network theory and 3-D Local Binary Pattern feature extraction process. We propose a novel face recognition algorithm based on adaptive 3-D Local Binary Pattern and Singular Value Decomposition method. In the process of face recognition, the most important part is facial feature extraction, by the way, Singular Value Decomposition method regards the face images as a matrix, and obtain image features by segmenting face images. The experimental simulation results show that our algorithm has good feature extraction effect and face recognition performance. We also compare our algorithm with other state-of-the-art methodologies and obtain the better effectiveness.
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