The Human Face Action Recognition System

Biometrics technology aims at utilizing major and distinctive characteristics such as behavioral or biological, for the sake of positively indentifying people. With the help of a combination of hardware and specific identifying sets of rules, a basic human attribute, automated biometric recognition mimics to distinguish and categorize other people as individual and unique. But the challenges surrounding biometrics are great as well.

In the present contemporary era, facial recognition technologies are being installed by the companies in an extensive sense that surely reflects a continuum of growing hi-tech superiority and complexity. At the most ordinary level, facial detection is done by this technology which means that a photo is just detected and located for a face ("Facing Facts: Best Practices for Common Uses of Facial Recognition Technologies," 2012).

2, supported by the given code writing which The conceptual model, Pseudo code, and Flowcharting but I come up with the approach to solve the problem, it start’s great started and finishing system flowcharting is so amazing, because flowcharting system is very specific graphic and structural and this is to grate and wonderful processing system ever to learn.

We applied ZFace \cite{jeni2015dense}, which is a person-independent tracker, in each video to detect the face and obtain 49 facial landmark points. The tracker outputs the 49 points' coordinates as well as a failure message to indicate the failure frames; those frames were excluded from further analysis. For each frame, we used the detected points to register and crop the infant's exact face region. We applied the tracker in 200 videos to detect the face and the landmark points.

The Algorithm Based Object Recognitions and Tracking (ABORAT) system offers the best solution for implementing a surveillance system based on automated behavior analysis. Its ability to interface with off-the-shelf products means

Facial recognition is a “one to many” process of searching a large database for individuals having similar features as the probe (Suspect). Facial Recognition is used as a “lead generator only” and should be considered as a tip only, it does not provide a positive identification of an individual. This is similar to inputting a latent print into the Automated Fingerprint Identification System (AFIS) and having ten candidates returned, they are only similar in nature.

Face recognition is another biometric technology. Face recognition uses the same technology that iris recognition uses. For face recognition, a camera takes several images of a person to find out who it is. Face recognition differs from all of the other biometric technologies because the person that is in the picture does not have to cooperate with the process. In all of the other technologies it requires the people to actively participate in the process. In face recognition, the image can be taken without the person even knowing that it took place.

Through this routine of advanced technology analysis, it has been established to increase the results and have hastened the procedure of identifying suspects of crimes. Facial recognition is also necessary for public involvement and observation as it also aids law enforcement officials to more easily zone in on possible suspects of a crimes being caught. With the use of facial recognition, it constantly has been proven quite an effective method with the incorporation of this technique.

This essay will talk about face recognition and several reasons why it has been studied separately. The ability to recognise faces is of huge significance of people’s daily life and differs in important ways from other forms of object recognition (Bruce and Young, 1986). Than this essay will talk about the processes involved in face recognition which comes from the diversity of research about familiar and unfamiliar faces-it includes behavioural studies, studies on brain-damaged patients, and neuroimaging studies. Finally, it will discuss how face recognition differs from the recognition of other object by involving more holistic or configuration processing and different areas of the brain (Eysenck & Keane, 2005).

Facial Action Coding System is a facial activity investigation framework that is generally acknowledged amongst researches. The framework or the system was initially created for mental purposes instead of computerized FER. It was proposed by Paul Ekman and Wallace V. Friesen in 1978. The essential key of the framework is to separation facial muscle ranges into 11 sections, and muscle development in those parts without hesitation units. The beginning framework had 44 AUs running from 1 to 26.

Biometric recognition plays an important role in the field of security for any system. For embedded system it plays a significant role. As we know ATMs and smart phones are the most used smart devices that we interact daily, is needs to be secured. For this reason it must have a secure system that can provide security to save our personal things. For securing them there are lots of ways that are introduced in the past years. Biometric recognition is one of them. In the field of biometric recognition face, fingerprint, voice and retina are used to recognize an individual. Iris recognition is one of them. (Jain, A.K, 2004) Every human has a different body characteristic. One will never be same like another. Eye is one of them.

The Facial Action Coding System (FACS) is an organization which classifies and the names the facial actions that occur on the human faces as it appears on the face. In the micro- expression recognition this organization has composed the facial movements based on the 38 elementary components [1]. These facial elements consist of 32 action units (AUs) and 6 action descriptors (ADs). Action units and action descriptor can be seen in the happening on the human faces in the form of facial expressions.

Bruce and Young’s theory of recognition tells us that human’s extract several kinds of information from faces; and that there are eight different components of such information. Such as structural encoding, expression analysis, facial speech analysis, directed visual processing, face recognition nodes,

Automatic face recognition has always been a major focus of research for a few decades, because of numerous practical applications where human identification is needed. Compared to other methods of identification (such as fingerprints, voice, footprint, or retina), face recognition has the advantage of its non-invasive and user friendly nature. Face images can be captured from a distance without interacting with the person, which is particularly beneficial for security and surveillance purposes. Furthermore, extra personal information, like gender, face expressions or age, can be obtained by further analyzing recognition results. Nowadays, face recognition technology has been widely applied to public security, person verification, Internet

Biometric technologies are getting better and finely tuned. The rate of false readings and errors has sharply fallen. However it still requires careful consideration and planning to implement a biometric identification system. They are most costly and complicated to implement as compared with other authentication systems. A proper evaluation of the system is important before purchasing any biometric system. A thorough risk analysis is necessary. In many cases biometrics may be overkill. Biometrics must be used if there is high level of risk involved. Customer acceptance is also important when logging on to company websites. Home users might not be ready to install biometrics on home computers for online banking.