Another biometric technology of face recognition verifies identity by matching feature data extracted from faces of more than one subject in a video or static image with feature data stored in the face database.

• Advantages & Disadvantages of Face Recognition Technology
• Advantages
  
  Unlike other biometric technologies, a user doesn't have to make physical contact with scanning equipment, so the user feels less strained during the process. As a result, he or she will not feel uncomfortable or offended.
  
  Sometimes, it is the only available option. In particular, it has significant implications in legal areas in case a picture is the only evidence available to identify a subject. For example, it is almost impossible to read and test the fingerprints or the retina of a criminal from a picture. But with the face recognition technology, it is possible to find the subject with only a picture.
  
  It doesn't need any expensive equipment to enter biological information. Only a regular video camera is needed.
  So, it is very simple compared to other equipment for other biometric technologies. In particular, since PC cameras are very popular at the moment, various kinds of application software for personal PC security are being released.
• Disadvantages
  
  It is somewhat difficult to develop a recognition system with excellent recognition rates in all environments since a face image can change widely depending on external elements such as light, noise, facial expressions, hairdo, posture, and so on, which, in turn, could interfere with correct face recognition.

• Face Recognition System Composition
  
  
  Figure 2.1 Five Stages of the General Face Recognition System
• Image Capturing
  
  After capturing an image from a CCD camera, store it.
• Preprocessing
  
  Eliminate noise from the image and separate the image.
• Detection
  
  Detect the face area from the image.
• Standardization
  
  Extract feature points and standardize brightness and geometry.
• Recognition
  
  Compare and recognize the detected image with images in database
  
  The process from numbers 1 to 4 is called "detection module", different from the "recognition module".
  The independent area of the recognition module is a preprocessing stage that affects overall performance of the recognition system.

• Face Recognition System Algorithm
• Detection Algorithm
  
  Before developing a fully automated face recognition system, face detection from an input image should be done first.
  The process is called "face area detection", and the overall performance of the system basically depends on the detection performance. General detection algorithms carry out the function of finding the area presumed to be a human face from the planar image.
  Its major methods are shown in Table 2.1 below.

  Types	Traits	Performance
  Nerve network	Search a learned face shape in a black and white static image.	Possible to detect more than two faces, but slow and difficult to learn faces.
  Nerve network +FFT	Apply this to black and white successive image frames in real time by using frequency spatial algorithm	Normally possible to track 1-2 faces in real time. Very difficult to learn.
  Fuzzy + nerve network	Use a fuzzy membership function as a value instead of pixel brightness value to enter in the nerve network.	Better performance than using the nerve network alone, but its processing speed is slower.
  RGB normalized color	Find the biggest skin color area only with color information using probability distribution.	Useful to find one face field, but possible errors in the background of the skin color.
  YIQ color+ other images	Combine color and movement information in the consecutive images of more than two.	Difficult to get a critical value that is not sensitive to red color and the diffused reflection of light.
  Fuzzy color	Modeling face color with a fuzzy membership function.	Widely influenced by membership function and knowledge base.
  PCA (Principal Component Analysis)	Find an image similar to a face by using a proper face as a basic vector.	Being used as an algorithm to extract a characteristic point more often than to recognize a face.
  Algorithm template	Detect a face by calculating correlation between facial geometric template and an image.	Difficult to respond to various changes in a face shape or face size.

  Table 2.1 Types of Detection Algorithms
  A static image is extensively used as an input image to extract a face. As for extracting a face, there is some difference between a static image and a sequence of static images photographed at regular intervals. In the case of one static image, it is easy to separate a face from the background with color information or Template by properly using several variables such as regular light or background, but if the image has complex background like a public place, it is very difficult to extract the face. On the other hand, it is relatively easy to extract a face using precise information from movement in a sequence of images due to very short time differences.
  The purpose of the extraction is basically to find a face by utilizing all kinds of image processing methods.
  So, there is no perfectly superior algorithm over others. Therefore, two or three algorithms among movements, color, Template, and artificial intelligence are being used together to maximize performance.

• Face Standardization Algorithm
  
  Standardization is a process that homogenizes the position, size and brightness of a face image captured from an extractor according to database standards to enhance overall recognition rate in a system. There are two main types of face standardization: geometric standardization and brightness normalization.
  A technique to extract a face area only with color or movement information can include its background other than the face or have some of the face area cut out according to the sensitivity of a color model or other images.
  The algorithm is used to extract feature points and eyes from the face and refer to the eye size and position in order to decide the final face area and standardize it geometrically. On the other hand, light normalization is the process of maintaining the same brightness degree on the input image regardless of changes in the environment.
  Like geometrical normalization, it can increase recognition rates by standardizing the brightness information about each pixel of an image. Almost all algorithms are applying brightness normalization, so they become insensitive to changes in brightness.
  Figure 2.2 below shows original face images and after images that went through brightness normalization.
  
  Face 2.2 Original face images and the Images that went through brightness normalization

• Face Standardization Algorithm
  
  Face recognition is the method that compares an input face image standardized according to database traits with all faces in the database for verification.
  Table 2.2 shows major algorithms relating to face recognition.

  Types	Characteristics	Performance
  Geometric approach	It verifies identity by comparing geometrical feature points of an input face image. Each face image should be changed in size and go through a standardization process. The positions of feature points are very critical.	A face is three-dimensional, can be hidden, and have various expressions. So, this approach has inevitable limitations.
  PCA	It considers bright and dark patterns of a planar image as a single vector, thinking of a face image as a series of those vectors.	If a face position or brightness is changed, it could recognize the one face as two different patterns.
  Probability approach	FLD, EFM, SVM, etc	An algorithm designed to boost its performance by working on the drawbacks of PCA.
  Nerve network	It teaches the system multi-level percept theory and applies it on a face image.	Learning has its own difficulties and it's hard to compose the learning data.
  Wavelet+Elastic Matching	It is effective to process changes in the position and expressions of a face by changing frequency.	Algorithm volume is too high compared to recognition rates.

  Table 2.1 Types of Detection Algorithms
  Technologies with the mixture of each element comprises the overall face recognition algorithm. The best -known algorithms are Eigenfaces, fisherfaces, ARENA, SVM-based, EFM-based, etc.
  
  Eigenfaces
  
  Eigenfaces was designed by Pentland in 1991. It applies PCA to extract feature points, and uses Euclidean distance to assess similarity. Its recognition rates are not high and respond sensitively to changes in lighting or environment.
  However, it is one of the most important face recognition methods, being used to compare with other algorithms.
  It is often referred to in many dissertations, and its performance is verified.
  
  Fisherfaces
  
  Fisherfaces was developed in 1997 based on PCA, and uses FLD (Fisher Linear Discriminant) as a classification algorithm. It also measures similarity with Euclidean distance easily. It learns characteristics of individuals so that it is more accurate and insensitive to external changes.
  In the case of off-line learning, it takes some time, but in the case of on-line learning, the characteristics can be applied to the system in real time.
  
  ARENA
  
  ARENA recognition algorithm is relatively simple, but effective to recognize a face from a 2D image.
  It employs PCA and SVM (Support Vector Machine) for face recognition. Its recognition rate is relatively high, but it consumes considerable time and memory to apply to a multi-class area such as face recognition.
  But SVM algorithm is diligently studied, so it is safe to say that it is one of the flagship algorithms in the face recognition area.
  
  EFM-based
  
  EFM-based face recognition method addresses the FLD generalization problems used in Fisherfaces by proposing and applying EFM to face recognition. It applied PCA before FLD-type processing for reducing dimensions.
  EFM-1 and EFM-2 are now proposed for EFM Algorithm. EFM-1 is aimed at reducing dimensions by maintaining Eigen values internally needed for variance matrix within a class, including the energy of maximum circle data, and selecting the Eigen values. Like Fisherfaces, EFM-2 reduces dimensions and processes the reduced variance matrix within the class.
  Then, feature points are selected among the values and other small, unselected Eigen values are included in the calculation of the variance matrix. According to experimental results, EFM-based face recognition method shows higher performance than Fisherfaces by 20 percent. Moreover, it has slightly higher perception rates than EFM-1 when EFM-2 Algorithm is optimized.