Number of input and output field

Number of input field	:	Two
Number of output field	:	One

This transformation function is used to calculate similarity of two faces. The output is a number (interval [0.0, 1.0]) that represent the similarity between them. A number value of 0.0 means that the two faces are completely different (or no faces are detected), whereas a return value of 1.0 means that they are identical.
This transformation function uses three parameters :

1. Face Detector
   method of face detector
2. Facial Feature Extractor
   method of face extractor
3. Key Point Descriptor
   Descriptor for comparing two faces

Available Face Detectors

• SandeepFaceDetector : A face detector that searches the image for areas of skin-tone that have a height/width ratio similar to the golden ratio. The detector will only find faces that are upright in the image (or upside-down).
• HaarCascadeDetector : A face detector based on the classic Viola-Jones classifier cascade framework. The classifier comes with a number of pre-trained models for frontal and side face views. The classifier is only mildly invariant to rotation, and it won't detect non-upright faces.
• FKEFaceDetector : The Frontal Keypoint Enhanced (FKE) Face Detector is not actually a detector in it's own right, but rather a wrapper around the HaarCascadeDetector. The FKE provides additional information about a face detection by finding facial keypoints on top of the face. The facial keypoints are located at stable points on the face (sides of the eyes, bottom of the nose, corners of the mouth). The facial keypoints can be used for alignment and feature extraction as described in the next section.
• CLMFaceDetector : The Constrained Local Model (CLM) face detector uses an underlying HaarCascadeDetectorto perform an initial face detection and then fits a statistical 3D face model to the detected region.
• IdentityFaceDetector : The identity face detector just returns a single detection for each input image it is given. The detection covers the entire area of the input image. This is useful for working with face datasets that contain pre-extracted and cropped faces.

Available Facial Feature Extractors

• CLMPoseFeature : A feature that represents the pose of a face detected with the CLMFaceDetector. The pose consists of the pitch, roll and yaw of the face.
• CLMShapeFeature : A feature that represents the shape parameters of a face detected with the CLMFaceDetector. The shape vector describes the shape of the face as a small set of variables.
• CLMPoseShapeFeature : A feature that represents the shape parameters and pose of a face detected with the CLMFaceDetector. The shape vector describes the shape of the face as a small set of variables, and the pose consists of the pitch, roll and yaw of the face.
• DoGSIFTFeature : A feature built by detecting local interest points on the face using a Difference of Gaussian pyramid, and then describing these using SIFT features.
• EigenFaceFeature : A feature built by projecting the pixels of an aligned face into a lower-dimensional space learned through PCA. The feature extractor must be “trained” with a set of example aligned faces before it can be used. This forms the core of the classic Eigen Faces algorithm.
• FaceImageFeature : A feature built directly from the pixels of an aligned face. No normalisation is performed.
• FacePatchFeature : A feature built by concatenating the pixels from each of the normalised circular patches around each facial keypoint from an FKEDetectedFace.
• FisherFaceFeature : A feature built by projecting the pixels of an aligned face into a lower-dimensional space learned through Fisher's Linear Discriminant Analysis. The feature extractor must be 'trained' with a set of example aligned faces before it can be used. This forms the core of the classic Fisher Faces algorithm.
• LocalLBPHistogram : Feature constructed by breaking the image into blocks and computing histograms of Local Binary Patterns (LBPs) for each block. All histograms are concatenated to form the final feature.
• LtpDtFeature : A feature built from Local Trinary Patterns (LTPs) within an aligned image. The features are constructed to be compared using a Euclidean Distance Transform with the LtpDtFeatureComparator or ReversedLtpDtFeatureComparator comparators.

The key point descriptor methods are :

• SIFT : (Scale Invariant Feature Transform) face-image descriptor.
• SURF : (Speeded-Up Robust Features) face-image descriptor.
• BRIEF : (Robust Independent Elementary Features) image descriptor
• ROOTSIFT : (Normalized Scale Invariant Feature Transform) face-image descriptor..
• FEATURE VECTOR : Face similarity is calculated base on feature vector.