Face Recognition Using Orthogonal Locality Preserving Projections

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.

Facial recognition is a technological product of the digital age that is continuing to evolve. The efficiency of the technology is an asset to crime detectives apprehending criminals, or the government tracking suspected terrorists for the safety of the country. It can also be used on a corporate level to allow a business to understand their ideal customer, or to increase the intractability of their website. While these abilities are positives to the people using them, the data collection may be harming more than it is helping. Considering the benefits of facial recognition, it is important for people to see what they are giving up for a more efficient experience. Facial recognition technology forces users to relinquish control of their own

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).

This double dissociation therefore suggests that recognition of faces and common objects is served by different mechanisms that are independent for each other. Although brain cells of face recognition have not been located, some functional models could help us interpret face recognition and prosopagnosia. One of the most influential models was proposed by Bruce and Young (1986). In this model face recognition involves several steps, and three steps more relate to prosopagnosia.

With the use of PCA, It becomes easy to reduce the complexity in images’ group (Karamizadeh et al., 2013).

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.

The testing procedure consisted of two computerized trials from DeSonneville, 2001.The first trial is called Face Recognition task from the Amsterdam Neuropsychological Task battery. It examines facial recognition, or identifying a familiar face among other faces. The second trial emphases proper identification of facial expressions using the Identification of Facial Emotion task from the Amsterdam Neuropsychological Task battery. Pre-trails were given to

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).

Prosopagnosia is defined as the difficulty in recognizing an individuals face; it is broken down into 3 main types; Apperceptive prosopagnosia, associative prosopagnosia and developmental prosopagnosia. Perception is an important aspect when recognizing faces, without recognizing the stimulus (face) you would not be able to identify a person. Therefore the individuals who sufferer from this disorder is unable to accurately recognize a face whether that be the face of a familiar person such as a family member or close friend, the face of a famous person, or even their own face. This paper will look at prosopagnosia in greater detail, it will explain the 3 main types, as well as give insight as to why face recognition is important in our

Facial point detection is necessary for tasks involving facial recognition. To represent facial expressions, point registration is often used. This process will typically involve the use of fiducial points to register complete features or parts of these facial features. AAM is often used to register fiducial points, however, there are other facial feature detectors that can be used just as effectively. \cite{NEEDED}\cmt{[1152]}, \cite{NEEDED}\cmt{[1162]}.

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,

Therefore, the original image space is highly redundant, and sample vectors could be projected to a low dimensional subspace when only the face pattern are of interest. A variety of subspace analysis methods, such as Eigen Face~\cite{turk1991eigenfaces}, Fisherface~\cite{belhumeur1997eigenfaces}, and Bayesian method~\cite{moghaddam2000bayesian}, have been widely used for solving these problems. One of the most useful methods is Mutual Subspace Method (MSM)~\cite{yamaguchi1998face}.