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Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using Principle Component Analysis and recognition using the feed forward back propagation...
This paper investigates how the application of simple weightless networks can be applied to complex patterns, in this case facial recognition. In this, the Generalised Convergent Network will be trained to recognise several classes (or people) and evaluated to see if it can be used to accurately identify various patterns that potentially belong to a class. The amount of processing time will be taken...
We present a low resolution face recognition technique based on a Convolutional Neural Network approach. The network is trained to reconstruct a reference per subject image. In classical feature-based approaches, a first stage of features extraction is followed by a classification to perform the recognition. In classical Convolutional Neural Network approaches, features extraction stages are stacked...
Driver's distraction in driving is one of the major causes of the traffic accidents. The abnormal behavior of the driver's head movement and the facial expressions were studied in detail in order to get the characteristics of the inattention status. With real-time monitoring on the driver's attention characteristics: the position and movement status information of eyes and mouth, the detection mechanisms...
Recognizing human emotions from facial expressions is highly dependent on the quality of the referred facial expression features. Conventional methods often suffer from high computation time and serious influence of environment variations. In this paper, a triangular facial feature extraction method based on a Modified Active Shape Model (MASM) is proposed. This method features considering the interactions...
Putting forward a face recognition method based on Diagonal Principal Component Analysis and BP neural network. Firstly, do the dimension reduction to the sample data and take the DiaPCA method to avoid the information drop; Then, use the classics BP neural network to do the face detection. It not only shorten the net training time, but also improve the accuracy of the recognition. It used 1000 face...
Eye detection is an important step for face recognition and verification because it provides a reference point to normalize not only location but also the flat 2d orientation of face relative to the image border. The base technique that is referred to shows how Wavelet Transformation works hand in hand with Neural Networks. In this paper a proposition of a system that regiment the wavelet coefficient...
The ability to recognize emotions in natural human communications is known to be very important for mankind. In recent years, a considerable number of researchers have investigated techniques allowing computer to replicate this capability by analyzing both prosodic (voice) and facial expressions. The applications of the resulting systems are manifold and range from gaming to indexing and retrieval,...
We present a comparison of three feature selection methods for face recognition: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). One also considers evaluation of the neural classifier based on Concurrent Self-Organizing Maps, (CSOM), previously introduced by first author of this paper. The ORL Database of Faces is used for experiments...
Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation...
Within the statistical decision theory framework, This paper focuses on specific objectives in face recognition, and proposes the BP neural network classification methods which is under the Linex loss function, and proves that the convergence of BP neural network under the Linex loss function. The image recognition experiments with the ORL face database in Cambridge show that this method can effectively...
In this paper a new approach to face recognition is presented which is based on processing of face images in hexagonal lattice. The importance of the hexagonal representation is that it possesses special computational features that are pertinent to the Human Vision process. Few advantages of processing images on hexagonal lattice are higher degree of circular symmetry, uniform connectivity, greater...
Human brain can detect faces from the images constructed in their eyes. The face detection is a computerize method of locating the face in the digital image. It is an important challenge to locate faces from uncontrolled and indistinguishable background of the digital image. This paper presents human face detection from the colored images. Skin color segmentation is used for localizations of skin...
In this paper we propose a novel approach to constructing a discriminant visual codebook in a simple and extremely fast way as a one-pass, that we call Resource-Allocating Codebook (RAC), inspired by the Resource Allocating Network (RAN) algorithms developed in the artificial neural networks literature. Unlike density preserving clustering, this approach retains data spread out more widely in the...
Good feature extraction scheme and classifiers are the key to face recognition algorithms. A general and efficient face feature extraction approach is presented which utilizes linear discriminant information and global search strategy. In order to get rid of redundant information and meanwhile reduce computational burden, we first compute the nonzero feature space of scatter matrix of the training...
The techniques of eigenfaces and neural net-based algorithms (LS-SVM and BP NNs) are combined to categorize gender from facial images in this paper. Based on exploration of the related techniques, the eigenfaces were firstly established from the training images, and the projection coefficients for training and testing images obtained in the space spanned by the eigenfaces; after that the LS-SVM and...
Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to face recognition tasks, there is always the need to construct large image repositories from people. Images...
In this paper, it is proposed a facial biometric identification system, using discriminative common vector. This method reduces the number of characteristics of the different images from the database and selects the most discriminative of them. In this work, transformed domains, such as discrete cosine transformed (DCT), discrete wavelets transformed (DWT), principal component analysis (PCA), linear...
Human brain can detect faces from the images constructed in their eyes. The face detection is a computerize method of locating the face in the digital image. It is an important challenge to locate faces from uncontrolled and indistinguishable background of the digital image. This paper presents human face detection from the colored images. Skin color segmentation is used for localizations of skin...
It is argued that for the computer to be able to interact with humans, it needs to have human communication skills. One of these skills is the ability to understand the emotional state of human. This paper describes neural network based approaches for emotion classification. We learn a classifier that can recognize 6 basic emotions with an average accuracy of 83% over the Cohn-Kanade database. The...
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