Face Recognition Essay

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).

Another common and inappropriate use of facial recognition technology exists in many social media platforms and systems. In the current social media domain,

In Till We Have Faces, C. S. Lewis retells the myth of Cupid and Psyche from the point of view of Psyche's sister, with powerful insight into the nature of human affection and the relationship between human and divine. In the original myth, Psyche is the youngest of three princesses, so beautiful that men begin to worship her instead of Venus. The goddess avenges herself by commanding that Psyche be exposed on a mountain to die, but her son Cupid secretly rescues her, having fallen in love with her. He hides her in a palace where he visits her nightly, but he forbids her to see his face for fear of his mother. After a time Psyche is granted a visit from her sisters; seeing the splendor of her palace, they are

The face recognition model developed by Bruce and Young has eight key parts and it suggests how we process familiar and unfamiliar faces, including facial expressions. The diagram below shows how these parts are interconnected. Structural encoding is where facial features and expressions are encoded. This information is translated at the same time, down two different pathways, to various units. One being expression analysis, where the emotional state of the person is shown by facial features. By using facial speech analysis we can process auditory information. This was shown by McGurk (1976) who created two video clips, one with lip movements indicating 'Ba' and other indicating 'Fa'. Both clips had the sound 'Ba' played over the clip.

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).

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.

Kakadiaris et al. [21] addressed the problem of deformation caused by large expression by fitting an annotated face model on facial surface. This model is well suited to study geometrical variability across faces and hence can model the deformation of face. Here the face annotation is fully automatic and they used advanced multistage alignment algorithms for matching the faces. The annotated face model is deformed elastically to fit each face, thus matching different anatomical areas such as the nose, eyes, and mouth. This work is able to recognize faces in presence facial expressions and it also provides invariance to 3D capture devices through suitable preprocessing steps. Here scalability in both time and space is achieved by converting

Age of Acquisition (AoA) - Acquiring information early on in childhood will result in quicker reaction time in recalling that information in adulthood. Significant amount of data was collected on speed of recall particularly in word identification and picture identification studies. This phenomenon was first identified by (Carroll and White 1973). Their finding was an important milestone as it demonstrates that priorly acquired names of objects are more likely to be absorbed into our memory, than the later obtained ones. Accordingly early acquired words are more efficiently and rapidly remembered, therefore reaction time is quicker than as it is with later acquired words.

Ear images can be acquired in a similar manner to face images, and a number of researchers have suggested that the human ear is unique enough to each individual to allow practical use as a biometric. The Alfred system presented by the researchers (Zhang. K., et. al., 2006) suggests that for identity recognition- ear shape can be used as a unique and comparable feature. The objective of this system was to extract ear shape features for recognition. Active shape model is used to model the shape and local gray texture of the ear and the steerable features are used to extract from the ear image ahead of active shape models to provide accurate guidance for shape location. Final classification is been proved to be successful in gait recognition is proved by Eigenshape. Ear database experiments shows an encouraging performance.

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

Most of the developed methods try to recognize the basic expressions and some attempts at recognizing non-basic expressions. However, there have been very few attempts at recognizing the temporal dynamics of the face. Temporal dynamics denotes to the timing and time length of facial actions. The significant terms that are used in joining with temporal dynamics are: onset, apex and offset. These are known as the temporal segments of an expression [40]. Onset is the instance when the facial expression starts, apex is the point when the expression is at its peak and offset is when the expression fades away (start-of-offset is when the expression starts to fade, and the end-of-offset is when the expression completely fades out). Similarly, onset-time is defined as the time taken from start to the peak, apex-time is defined as the complete time at the peak and offset-time is defined as the total time from a peak to the stop. Pantic and Patras have reported successful recognition of facial AUs and their temporal segments [2]. By doing so, they have been able to recognize a much larger range of expressions.