International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 07 | July-2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
A hybrid approach to recognize facial image using feature extraction method Swati Yadav , 2Ajay Phulre
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M-Tech Scholar, 2Asst. Professor 1,2 CSE Department, SBITM, Betul, M.P., India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Today's era is of data science and this data
computational complexity and recognition performance. Face recognition systems are built on the idea that each person has a particular face structure, and using the facial symmetry, computerized face-matching is possible. The work on face recognition has begun in the 1960’s, the results of which are being used for security in various institutions and firms throughout the world. The images must be processed correctly for computer based face recognition. The face and its structural properties should be identified carefully, and the resulting image must be converted to two dimensional digital data. An efficient algorithm and a database which consists of face images are needed to solve the face recognition problem. In this paper, Eigenfaces method is used for face recognition. In the recognition process, an eigenface is formed for the given face image, and the Euclidian distances between this eigenface and the previously stored eigenfaces are calculated. The eigenface with the smallest Euclidian distance is the one the person resembles the most. Simulation results are shown. In this category a single feature vector that represents the whole face image is used as input to a classifier. Several classifiers have been proposed in the literature e.g. minimum distance classification in the eigenspace , Fisher's discriminant analysis [13], and neural networks [6]. Global techniques work well for classifying frontal views of faces. However, they are not robust against pose changes since global features are highly sensitive to translation and rotation of the face. To avoid this problem an alignment stage can be added before classifying the face. Aligning an input face image with a reference face image requires computing correspondences between the two face images. The correspondences are usually determined for a small number of prominent points in the face like the centre of the eye, the nostrils, or the corners of the mouth. Based on these correspondences the input face image can be warped to a reference face image. In [I21 an affine transformation is computed to perform the warping. Active shape models are used in [10] to align input faces with model faces. A semi-automatic alignment step in combination with SVM classification was proposed in [9].
science is applied on many of the area like image analysis, transportation analysis, big data analysis and many more .Out of these area image is one of the biggest area on which various data analysis methods has been developed to find the different outcomes. So our proposed work is also on the image area where we will mainly consider the face image because we have seen many difficulties in recognizing face when there are variations in the images due to lighting and other disturbing conditions. In this work we mainly consider faces based on eigen vectors and we will apply feature selection method i.e. Principal Component Analysis, this approach is mainly applied to reduce the dimension of the feature vector. This approach mainly selects the best feature vectors which increase the classification accuracy. After that we will apply classification by using SVM to get the desired result.
Key Words: Eigen faces, PCA, SVM, Facial INTRODUCTION Over the past 40 years numerous face recognition papers have been published in the computer vision community; a survey can be found in [4]. The number of real world applications (e.g. surveillance, secure access, human computer interface) and the availability of cheap and powerful hardware also lead to the development of commercial face recognition systems. Despite the success of some of these systems in constrained scenarios, the general task of face recognition still poses a number of challenges with respect to changes in illumination, facial expression, and pose. In the following we give a brief overview on face recognition methods. Focusing on the aspect of pose invariance we divide face recognition techniques into two steps: (i) Feature Extraction (ii) Dimensionality reduction. A variety of facial feature extraction method and face recognition system that is a combination of classifier have their own strengths and weaknesses in
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Impact Factor value: 5.181
The face recognition system is similar to other biometric systems. The idea behind the face recognition system is the fact that each individual has a unique face. Similar to the fingerprint, the face of an individual has many
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