Facial feature detection and face recognition from 2D and 3D images

A Survey of Face Recognition Techniques

“…Such methods make use of the PCA of intensity images [242][243][244], facial profile intensities [245], Iterative Closest Point (ICP [246]) [247,248], Gabor wavelets [249], and Local Feature Analysis [250], etc. For instance, Wang et al [249] extract 3D shape templates from range images and texture templates from grayscale images of faces, apply PCA separately to both kinds of templates to reduce them to lower-dimensional vectors, then concatenate the shape and texture vectors and, finally, apply SVMs to the resulting vectors for classification. In general, experiments with such systems indicate that combining shape and texture information reduces the misclassification rates of the face recognizer.…”

Feature Extraction and Representation for Face Recognition

“…a 100x100 image would result in a 40x100x100=400000 dimensional feature vector. Some authors used Kernel PCA to reduce this dimensionality termed as Gabor-KPCA (Liu, 2004), and others (Wu and Yoshida, 2002;Liu et al, 2004;Wang et al, 2002) employ a feature selection mechanism for selecting only the important points by using some automated methods such as Adaboost etc. Nonetheless, a global description in this case still results in a very high dimensional feature vector, e.g.…”

“…Nonetheless, a global description in this case still results in a very high dimensional feature vector, e.g. in (Wang et al, 2002) authors selected only 32 points in an image of size 64x64, which results in 32x40=1280 dimensional vector, due to this high dimensionality the recognition is usually performed by computing directly a distance measure or similarity metric between two images. The other way can be of taking a bag-of-feature approach where each selected point is considered an independent feature, but in this case the configural information of the face is effectively lost and as such it cannot be applied directly in situations where a large pose variations and other appearance variations are expected.…”

Facial Recognition using Modified Local Binary Pattern and Random Forest

“…However, recognition accuracies substantially decrease when the captured images do not have enough quality either due to subject's alignment problem to the camera, various facial expressions, gaze deviations or facial hair [1][2][3][4][5]. Several researchers proposed different facial recognition algorithms that perform well with unconstrained face images [1][2][3][5][6][7][8][9]. Recently, the face recognition algorithms based on local appearance descriptors such as Gabor filters, SURF, SIFT, and histograms Local Binary Patterns (LBP) provide more robust performance against occlusions, different facial expressions, and pose variations than the holistic approaches [10,11].…”