Facial feature extraction using topological methods

Abstract

Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recognition, facial image compression and low-bit video coding. The methodology presented in this pper, considen the facial image as a surface. Topological properties of the facial surface, such as principal curvatures are used to extract the eyes and mouth, which form deep valleys on the surface. Ravines are points on the surface where the maximum cuwature is a local maximum in the corresponding principal direction. The basic idea of the proposed method is lo model the facial features as ravines on the facial surface. Experimental results have shown accurate extraction of the eye boundaries and the mouth opening in a very small computational time.

Own-race and own-species advantages in face perception: a computational view

“…In the following we use face database 1 to evaluate a learning process toward another species and face database 2 to evaluate a similar effect toward another race. From all face data sets we extracted face features by implementing the topological methods suggested by 21 . In Figures 1A and H typical face images are shown together with the extracted features.…”

“…We implemented facial feature extraction according to 21 , making use of the topological features of faces to localize eyes, nose and mouth. With the image taken as surface and values on the z-axis as image intensity, eyes, nose and mouth are singularities in the image, forming valleys and peaks on the luminance surface.…”

“…As proposed in an earlier model 10 , we assume that the neural machinery for face discrimination has access to complex non-linear face features, representing the facial features as extracted from high-dimensional face space by means of sensory processing. While in this previous model the facial features are artificially generated normal distributions of feature samples, we here extract facial features using topological methods 21 and test the model predictions under the scenarios of the own-race and own-species advantages. We here show that under assumption that the visual system is generically plastic and under consideration of the exposure history, the own-race and own-species advantages can be explained with the same underlying computational mechanism.…”

A comparative study of face landmarking techniques

“…A commonly occurring theme is the heuristic segmentation or compartmentalization of the face encompassing target regions of interest [39,[41][42][43]. For example, the face is partitioned with a grid structure resulting in two or more horizontal and vertical stripes.…”

Weighting Facial Features Extraction using Geometric Average

“…Many research on feature extraction with various methods and algorithms, for example using genetic algorithms [1], eigenface [3], Smallest Univalue Segment Assimilating Nucleus (SUSAN) [2], Complex Dual-Tree Wavelet Transform [4], Gabor Filter [5], Active Shape Model [6] and Enhanced Active Shape Model [7]. Facial feature extraction can also be performed based on the position of the geometry of facial features [8]. The above studies produce varying degrees of accuracy, have their own disadvantages and advantages and also use techniques to calculate different levels of accuracy.…”