The proposed method is based on eigenspaces [14] and it is obtained with the Principal Component Analysis (PCA) [15] of the vectorized set of three features include WF, FFP, and RFF. Localizing the facial feature points is obtained by a novel algorithm. For this purpose, we briefly review some of the previous methods in this section.
2.1 Face Recognition Using Principal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique based on extracting the desired number of principal components of the multi-dimensional data. PCA is closely related to the linear Karhunen–Loève Transform [16]. The feature vectors for PCA when used in face recognition [1] are vectorized face images. These raw feature vectors are very large and are highly correlated. PCA rotates feature vectors from this large, highly correlated subspace to a small subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace has no sample covariance between features. Therefore, not useful information such as lighting variations or noise is truncated and the remaining basis vectors are used to reconstruction the training data, i.e. subspace.
When a test image was projected into the subspace, Euclidean distances between its coefficients vector and those representing each subject were computed. Depending on the distance to the subject for which this distance would be minimized, the image was classified as belonging to one of the familiar subjects, as a new face, or as non-face.
2.2 Face Recognition Using Linear Discriminant Analysis
When substantial changes in illumination and expression are present, much of the variation in the data is retained due to the PCA techniques, and...
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In geometric feature-based approaches, the features are extracted using anthropometric relation of the face components [20]. Analysis of horizontal and vertical edge projections are such examples [21]. Template-based approaches match facial components using appropriate energy functional. The best match of a template in the facial image will yield the minimum energy. Template matching [22], ASM [23] fall into this category. Color segmentation techniques [24] use skin color to isolate the face. Any non-skin color region within the face can be represented as a candidate for “eyes” and/or “mouth”. Appearance based approaches aim to find a pattern automatically from a test dataset and then search the input image for the pattern. Methods such as Hidden Markov Model [25], SVM, and AdaBoost [26] are used to extract the feature vector containing the facial components.
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