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The Eigenface method is a classic face recognition method. This article is based on the method of Eigen face to recognize the facial expression. The aim of this method is to recognize the facial expression stored in a database. It uses a set of single static image with different expression labels as the training database, projected the training image to subspaces. The similar face of the tested expression...
This paper describes the different classifier methods with minimum means of clusters to achieve face recognition rate of humans from the feature extracted of training face image data for many sets of images as a data base. Principal Component Analysis (PCA) is a robust method used as feature extraction techniques for face recognition but the recognition decreases with the variation of person's actions...
Sign language is widely used by individuals with hearing impairment to communicate with each other conveniently using hand gestures. However, non-sign-language speakers find it very difficult to communicate with those with speech or hearing impairment since it interpreters are not readily available at all times. Many countries have their own sign language, such as American Sign Language (ASL) which...
In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
Sign language is a way to communicate for deaf people, which hand shapes are used instead of sound patterns. In this paper, we present a method for recognizing alphabet and numbers in American sign language based on saliency detection of the image. After detecting saliency, the images were processed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), in order to reduce dimensions...
The increased need of person verification in daily life created a big market for biometric machine authentication tools. A few years back, fingerprint verification was done only in criminal investigation. Now finger-prints or face-images are widely used in bank tellers, airports, building entrances. Due to natural inhibition to allow finger-prints, and physical difficulties to procure it, its popularity...
This paper consists of development of detection strategies for face recognition tasks and to access its feasibility for forensic analysis using the FERET face database Author has used global feature extraction technique using statistical method for image classification. Facial images of three subjects with different expression and angles are used for classification. Principal Component Analysis has...
Cursive scripts such as Urdu, Pashto and Arabic contain large number of unique shapes called ligatures. Recognition of thousands of ligatures is challenging due to variations of various kinds including scaling, orientation, font style, spatial location/registration of ligatures and limited number of samples available for training. Accurate segmentation is a key challenge for analytic approaches, whereas...
Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers...
This paper examines the Kernel Principal Component Analysis (KPCA) feature detection and classification for underwater images. In Underwater images the numbers of distortion occurred are blurring of image, illumination of light and rotation of angle, noise etc. Features are normally extracted by the method called SIFT (Scale Invariant Feature Transform for underwater images). It is used for extracting...
We have developed the prototype of a system that recognizes the denomination of the largest U.S.A. currency notes in circulation in Ecuador, aimed at visually impaired people. It is capable of reproducing audio messages that announce the denomination of a banknote in front of a smartphone camera by processing each frame of its continuous filming. This work takes its theoretical basis from the Digital...
In the palmprint identification system, feature fusion has been emerging an effective way to improve system performance. Feature vectors extracted from palmprint and fusion strategy are key factors in this procedure. This paper discusses issues about the two aspects respectively. (1) The performance will decline considerably as the number of categories increase. This is determined by the intrinsic...
This paper presents a novel texture recognition method using bispectrum slice. The first step, Radon transform, was to reduce the dimension of the image data. The second step was to calculate bispectrum and extract bispectrum diagonal slices as texture features. The third step was to apply principal component analysis(PCA) for reducing the dimension of feature vectors. Finally, BP(Back Propagation)...
As the angle and intensity of light may change in practical cases, it is difficult to measure the illumination of an image. Taking into account the characteristics of the image illumination conditions, we propose a new method based on bilateral-filtering algorithm to enhance the illumination invariant from the face image, then estimate the compensation image by dividing the original image, and finally...
Insect recognition is the basis of crop pest and disease control. Traditional insect recognition methods are time-consuming and hard-labor. Automatic machine insect recognition can solve the problem. In this paper, spectral regression LDA is used to reduce high dimension spaces of insects images, and get insect feature subspace. Then coefficient vector in the subspace is taken as the input of KNN...
We explore sparse regression for effective feature selection and classification in face identity and expression recognition. We argue that sparse regression in pixel space is inappropriate. We propose instead a method which combines the virtues of sparse regression with projection methods such as PCA and FDA. The method can learn a sparse set of discriminative projections and increase recognition...
Segmentation is the foundation for image processing, and it is also one of the basic works in image recognition and image analysis. This paper introduces SOM neural network and takes out principal component analysis with 12 components on the four colorful spaces RGB, YIQ, XYZ and Ycbcr. Then the methods 3PCA, the 2PCA and the 1PCA are used respectively to segment the color milk somatic cell images...
In this study, iris recognition in the presence of partial occlusions is investigated using holistic and subpattern-based approaches. Principal Component Analysis (PCA) and subspace Linear Discriminant Analysis (ssLDA) methods are used as feature extractors to recognize iris images. In order to eliminate the effect of illumination changes, histogram equalization and mean-and-variance normalization...
In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, a new approach is proposed that the low frequency sub-images are obtained by utilizing two-dimensional wavelet transform and then the features are extracted by applying Orthogonal Centroid Algorithm...
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