The proposed method is based on eigenspaces [14] and it is obtained with the Principal Component Analysis (PCA) [15] of the vectorized set of three features include WF, FFP, and RFF. Localizing the facial feature points is obtained by a novel algorithm. For this purpose, we briefly review some of the previous methods in this section.
2.1 Face Recognition Using Principal Component Analysis
Principal Component Analysis (PCA) is a dimensionality reduction technique based on extracting the desired number of principal components of the multi-dimensional data. PCA is closely related to the linear Karhunen–Loève Transform [16]. The feature vectors for PCA when used in face recognition [1] are vectorized face images. These raw feature vectors are very large and are highly correlated. PCA rotates feature vectors from this large, highly correlated subspace to a small subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace has no sample covariance between features. Therefore, not useful information such as lighting variations or noise is truncated and the remaining basis vectors are used to reconstruction the training data, i.e. subspace.
When a test image was projected into the subspace, Euclidean distances between its coefficients vector and those representing each subject were computed. Depending on the distance to the subject for which this distance would be minimized, the image was classified as belonging to one of the familiar subjects, as a new face, or as non-face.
2.2 Face Recognition Using Linear Discriminant Analysis
When substantial changes in illumination and expression are present, much of the variation in the data is retained due to the PCA techniques, and...
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In geometric feature-based approaches, the features are extracted using anthropometric relation of the face components [20]. Analysis of horizontal and vertical edge projections are such examples [21]. Template-based approaches match facial components using appropriate energy functional. The best match of a template in the facial image will yield the minimum energy. Template matching [22], ASM [23] fall into this category. Color segmentation techniques [24] use skin color to isolate the face. Any non-skin color region within the face can be represented as a candidate for “eyes” and/or “mouth”. Appearance based approaches aim to find a pattern automatically from a test dataset and then search the input image for the pattern. Methods such as Hidden Markov Model [25], SVM, and AdaBoost [26] are used to extract the feature vector containing the facial components.
By definition Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic (Campbell, 1995). More and more businesses are now using biometrics as a preferred measure over traditional methods involving passwords and PIN numbers for 2 reasons; The person being identified is required to be physically present at the point of identification; Identification based on biometrics techniques removes the need to remember a password or to carry other identification (Watrall, 10/14/03). The need for biometrics can be found in federal, state and local governments, in the military, and in commercial applications (Campbell, 1995). Enterprise-wide network security infrastructures, government IDs, secure electronic banking, investing and other financial transactions, retail sales, law enforcement, and health and social services are already benefiting from these technologies (Campbell, 1995).
Multiscale PCA (MSPCA) combines the capability of PCA to extract the cross-correlation between the variables and wavelets to divide deterministic features from stochastic processes and approximately de-correlate the autocorrelation among the measurements. Figure 2.3 illustrates the MSPCA procedures.
...ge flow and pattern types, are prominent enough to align fingerprints directly. Nilsson [26] detected the core point by complex filters applied to the orientation field in multiple resolution scales, and the translation and rotation parameters are simply computed by comparing the coordinates and orientation of the two core points. Jain [27] predefined four types of kernel curves:first is arch, second is left loop ,third is right loop and fourth is whorl, each with several subclasses respectively. These kernel curves were fitted with the image, and then used for alignment. Yager [28] proposed a two stage optimization alignment combined both global and local features. It first aligned two fingerprints by orientation field, curvature maps and ridge frequency maps, and then optimized by minutiae. The alignment using global features is fast but not robust, because the
Image quality assessment is another step in image processing in which statistical parameters are used to measure the quality of the processed image in reference to the raw image or the original image. We shall discuss that later in Chapter 5.
Face perception is when someone is able to analyze and interpret the face, mainly the human face. In this particular case, the perception is in regards to infants. Recognition is defined in a similar manner. It is when something has been previously seen or heard. Face perception during early infancy (Article 7) by Mondloch, Lewis, Budreau, Maurer, Dannemiller, Stephens, and Gathercoal does a great job explaining young infants face perception and recognition. In this article, the researchers decided to conduct an experiment on newborns, 6-week-olds, and 12-week-olds. They used a standardized method, which was called the Teller Acuity card procedure. This procedure was when an observer did not know what was presented each trial and tried to see if the infants preferred one of the stimuli, or cards, over another. There were five cards in total. Three were the experimental cards and two were the control, or tester, cards. For the experimental cards, one card consisted of a config and its inversion. A config is an outline of a head shape and it has three black dots inside the shape forming a set of eyes and mouth. The inversion is when the “config” is flipped upside down. The second card consisted of a spectrum of a face and its’ amplitude spectrum. The amplitude spectrum was like the opposite. It was a fuzzy spectrum and you couldn’t see a face. The third card consisted of a positive and negative contrast face. One of the faces was a positive contrast and the other face was a negative contrast. For the control cards, these cards were used to test the validity of the card procedure and it tested every age. Both of the cards consisted of wide black and white ...
The importance of mean and covariance- There is no guarantee that the directions of the maximum variance will contain good features for discrimination.
Feature extraction on the basis of principle lines: Any palm print have several principal lines in it, on the basis of these feature extraction is quiet useful for recognition and extraction of palm print recognition system.
As I mentioned above both S.P. and the twenty control subjects were all measured on facial affect evaluation, lexical affect identification, and facial affect generation. Facial affect evaluation consisted of all subjects being given a facial expression term (e.g., afraid, angry, disgusted, happy, sad, and surprised), once given a term they were showed a facial expression and asked to rate on a scale
When the complete set of principal component variables Y is given, it is found that a MEWMA chart applied to Y generates the same value of T^2 as applying MEWMA to original variables, X. [6]
Facial recognition is a process that allows human beings to identify other human beings simply from the structure of their faces, and their facial features (Nugent, 2017). However facial recognition is not the only form of recognition humans can use; object recognition although very different from facial recognition allows for human beings to identify an object from a photograph, or the object being described to them, they are aware of the pattern and structure of the object.
In 3.1 sections, if the background colour has the same skin pixel with the human face, it will certainly become a problem in detecting human face. So in this step, we not only prepare eyes, nose and mouth data sets but also for the face to increase the accuracy in detecting face.
Facial expression recognition (FER) was determined by child scores on two FER tasks: the emotion-matching task and the emotion-labeling task. Children and their parents in the focus cohort were invited to the Generation R Research Center when participants were 36-months old. The final study consisted of 808 children with data on both FER tasks. During the emotion-matching task, images of human faces depicting four emotions (happiness, sadness, anger and fear) were presented to the children on a touch sensitive monitor. Children were presented two faces on the bottom of the screen, and one image at the top of the screen. They were instructed to choose the face that matched the emotion of the target face using the touch-sensitive monitor. There
The human face is an effective, important and composite communication medium. While a person speaks, the expressions in the face changes frequently. Those expressions are related to both emotions and the flow of speech. It is noted from the studies that speaking is very important for conveying different expressions. Moreover, many psychologists have found out that facial expressions resemble the emotions and attitudes of different people. Hence, in order to improve the systems to produce effective facial expressions, it is important to understand such a language. Computer facial animation is mainly a part of Computer Graphics that binds techniques and models to produce and animate human face and head. It is also associated with the fields like traditional animation and psychology because of its subject type and productivity.
After detect the center of pupil and the corneal reflection, the vector between them is used to determine the gaze direction as shown in Figure 1 56, This technique has ability to eliminate the optical reflective effect of accessories and glasses.[36]
Iris recognition is very accurate and distinctive because iris has a complex texture that can produce a substantial amount of information to identify a person. Furthermore, the iris remains almost unchanged from childhood, only minuscule variations are presented. The biometric data is captured using a small and high definition camera that is able to recognize different characteristics of the iris. Moreover, the system can detect the use of contact lens with a fake iris and can realize with the natural movement of the eye if the sample object is a living being. Although initially iris recognition systems were expensive and complex to use, new technology developments have improved these weaknesses.