Description:FIELD OF THE INVENTION
This invention relates to Face Recognition through Principal Component Analysis Algorithm
BACKGROUND OF THE INVENTION
Principal Component Analysis (PCA) is a computer application for automatically recognizing face of a person or to identify a person from digital image, using reduced face data. PCA is an appearance based method.
One dimensional vector from pixels are framed from two dimensional face image. The pixels are now principal components of face feature space, known as eigenspace. PCA is a compression method. Mean square error between the original image and reconstructed image is reduced much.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Face recognition method automatically recognizes or identifies the face of a human from digital image. PCA is an appearance based method to recognize face images. PCA is an eigenvector algorithm to consider linear variation in high-dimensional data. This approach is used to reduce the dimensions of face related data which are having strong correlation, thousand times with the help of small intrinsic dimensional features which are independent variables. This dimensionality reduction method reduces the usage of unrelated details in face recognition of individual. PCA algorithm decomposes the face image into uncorrelated components, named as, eigenface. The face image is mentioned as feature vector. Feature vector, present in one dimensional array, is equal to the weighted summation of the eigenfaces. For enhancing the performance, the face image supplied every time.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Face recognition method automatically recognizes or identifies the face of a human from digital image. PCA is an appearance based method to recognize face images. PCA is an eigenvector algorithm to consider linear variation in high-dimensional data. This approach is used to reduce the dimensions of face related data which are having strong correlation, thousand times with the help of small intrinsic dimensional features which are independent variables. This dimensionality reduction method reduces the usage of unrelated details in face recognition of individual. PCA algorithm decomposes the face image into uncorrelated components, named as, eigenface. The face image is mentioned as feature vector. Feature vector, present in one dimensional array, is equal to the weighted summation of the eigenfaces. For enhancing the performance, the face image supplied every time.
Minimum number of Eigenfaces with hight eigenvalues are taken for recognition. Training images for one person are more for increasing the detection rate. Face recognition is a two dimension method. Here is an example. Eye is an important organ. The distance or angle between the eyes are measured to detect the faces.
PCA predicts, reduces the data usage, extracts the features and compresses the data.
In the process, first training images are created for human faces. Principal components are obtained then. The test faces are projected on the principal components. The distance between the test and training images predicts the test face.
NOVELTY:
PCA is used to identify the individual’s face from the face images, requiring less face data or
small set of face related features to approximate the known facial images.
ADVANTAGES OF THE INVENTION
1. Recognition speed is more.
2. Accurate face detection.
Reason: If the distance between the test image and trained image is zero or slightly greater or less than threshold, then test image is matching with the training dataset image and the person is considered as known.
, Claims:1. A method for face recognition comprising the steps of:
a) capturing a digital image of a human face;
b) converting the image into a feature vector using Principal Component Analysis (PCA);
c) decomposing the image into a plurality of uncorrelated components known as eigenfaces; and
d) recognizing the face by matching the feature vector with weighted summations of selected eigenfaces having highest eigenvalues.
2. The method as claimed in claim 1, wherein the PCA algorithm reduces the dimensionality of face image data by extracting independent intrinsic features from correlated high-dimensional input data.
3. The method as claimed in claim 1, wherein the recognition of the test face is performed by projecting it onto the principal components obtained from training images, and computing the distance between the test image and training images in the PCA feature space.
4. The method as claimed in claim 1, wherein the training phase includes using multiple facial images of an individual to enhance detection accuracy and improve recognition performance.
5. The method as claimed in claim 1, wherein the face recognition process is a two-dimensional method and includes measurement of facial features such as distance or angle between the eyes as part of the feature extraction process.