Sentence examples for normalized faces from inspiring English sources

The third and last step recognizes the detected and normalized faces and assigns identities to them.

In [26], normalized faces are used to train an auto-associative memory using the Widrow-Hoff correction rule in order to classify head poses.

The DM- AAMs are more powerful than the classical ones when used with normalized faces with variable illuminations (CMU-PIE database), but are useless in standard situations (BioId database).

Määttä et al. combined three different LBP configurations ( LBP 8, 2 u 2, LBP 16, 2 u 2 and LBP 8, 1 u 2 ) in a normalized face image and trained a support vector machine (SVM) classifier to discriminate real and fake faces.

selection of half of a normalized face image and application of LBP to this half-image in order to generate a feature vector containing only uniform patterns; usage of the Adaboost method for the "gender" attribute to exclude insignificant features from the image feature vector (the sum of weight coefficients of the boosted features is equal to 0.95).

This can be observed in Figure 3a.In Figure 3, the gradient of the rgbGE normalized face images (three images of a subject) is compared with the original image without correction and with various illumination normalization methods such as LT, HE, and GC.

The shape of significant facial components is retained by extracting gradient magnitude information from the rgbGE-normalized face image so as to minimize the intra-subject variations due to surgery.

We used both inter-subject and intra-subject methodologies for training and testing the proposed architecture, but the process is similar for both: the video frame containing the frontal face is normalized, the face is detected using the Viola-Jones face detection algorithm [68], and the same algorithm is used for detecting the face components: the eye, brow, cheek, wrinkles, and lips [69].

Furthermore, for the same deformation mode the normalized back face sheet deflection increased linearly with impulse.

Figure 9 shows an original single-band image together with the normalized spectral-face image in the left column.

The assessment of quality is based on the assumption that given a normalized frontal face image, the location of SIFT-based feature points will be symmetric with a vertical axis.