Facial recognition Software: Basics
Facial recognition software is a well designed software application to identify the available person’s picture (face) with the video or picture in the database. Face recognition software is developed on the principle of the ability to recognize the face & then compare various features of the face. Every face has different landmarks, peaks & valleys. The landmarks, by many software companies, are usually referred as nodal points. In general, every human face has 80 nodal points, and these nodal points are measured creating the numeric codes called as face print hence referred as face in the software database.
The widely referred nodal points are distance between
– Two eyes depth of the eye sockets
– Shape of cheek bones
– Width of the nose
– Length of the jaw line
The recognition algorithm can be divided geometric & photo-metric methods. The geometric algorithm uses the distinguishing features of images while as photo-metric algorithm is the statistical approach that distills an image into certain values & compares the values with the templates.
Popular algorithms used for facial recognition include
Principal Component Analysis using eigenfaces : This type of facial recognition software uses the set of eigenvectors (characteristic vector) when they are used in the computer vision problem of human face recognition
Linear Discriminate Analysis: This type of facial recognition software uses the method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier or, more commonly, for dimensionality reduction before later classification.
Elastic Bunch Graph Matching using the Fisher face algorithm: This type of facial recognition software uses the pattern recognition techniques in computer science. Elastic matching (EM) is also known as deform-able template, flexible matching, or nonlinear template matching. Elastic matching can be defined as an optimization problem of two-dimensional warping specifying corresponding pixels between subjected images.
The Hidden Markov model: This type of facial recognition software uses the statistical model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
The Multi-linear Subspace Learning using tensor representation: Tensors are geometric objects that describe linear relations between geometric vectors, scalars, and other tensors. This type of facial recognition uses the dimensionality reduction can be performed on data tensor whose observations have been vectorized and organized into a data tensor, or whose observations are matrices that are concatenated into a data tensor. Here are some examples of data tensors whose observations are vectorized or whose observations are matrices concatenated into data tensors images (2 D/3 D), video sequences (3 D/4 D), and hyper spectral cubes (3 D/4 D).
The neuronal motivated dynamic link matching: The neuronal model for face recognition. It uses wavelet transformations to encode incoming image data. Bunch graph matching is an algorithm based on many ideas found in dynamic link matching
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