Chi-Square (CSQ) Method
Chi-square (CSQ) statistic calculates the goodness-of-fit of the data to the model.
That is, Chi-square is the sum of the squared difference between observed (\\({\\mathbf{O}}\\)) and the expected (\\(\\varepsilon\\)) data (or the deviation, \\(\\delta\\)), divided by the sum of observed and expected data in all possible categories.
If the observed values i.
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How are facial recognition systems classified?
In this review paper, we will classify these systems into three approaches based on their detection and recognition method ( Figure 2 ):
- (1) local
- (2) holistic (subspace)
- (3) hybrid approaches
The first approach is classified according to certain facial features, not considering the whole
face.
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How do we analyze the features of face recognition?
To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition.
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Hu Moment Invariants
Hu [32] first introduced two-dimensional geometric moment invariants concept to apply for shape recognition task.
A set of seven nonlinear moment functions are derived from the second and third order moment, which are translation, scale and rotation invariants.
A digital image \\({\\fancyscript{f}}\\left( {{\\fancyscript{a}},{\\fancyscript{b}}} \\right)\\.
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Is a face recognition method based on statistical features and support vector machine?
In this paper, a face recognition method based on statistical features and Support Vector Machine (SVM) algorithm is proposed.
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Moments and Moment Invariants
Moment concept is mainly used for shape descriptor of a probability distribution function and use to many real-world applications such as computer vision, image processing and pattern recognition areas for object matching, recognition, classification and identification purposes.
Mathematically, moments are “projection” of a function onto a polynomi.
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Standard Deviation of Fifteen Hu Moment Invariants
The three types of Hu’s invariant moment values such as \\(\\varepsilon\\) i, \\(\\varphi\\) i and \\(\\zeta\\) i are computed using the following equations [4] (32–36): where i= 1,2,3,4,5, \\(\\psi_{z}^{\\text{Ref}}\\) and \\(\\psi_{z}^{\\text{Test}}\\) are the HuMIs of binary form of test and reference images, respectively.
It is invariant to scale, rotation and .
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Which statistical feature analysis methods are based on GLCM in face recognition?
The outstanding statistical feature analysis methods are based on GLCM in face recognition.
There are some enhancements which are implemented such as:
- GLDM and GLRLM [ 29
- 30 ]
The latest work is from GLCM that extracted different features of the face based on GLCM [ 31, 32 ].
However, in the existing methods, there are some drawbacks.