Occlusion the performance of the face recognition algorithms under occlusion is in general poor.

Another common and inappropriate use of facial recognition technology exists in many social media platforms and systems. In the current social media domain, when you post or are tagged in a photo, you are, in essence, saying goodbye to anonymity and the right to privacy that we value socially and culturally. Photos from Facebook, Twitter, and Instagram can all be used online to create an unintended profile: your “face print”. Using this “face print”,

According to this hypothesis, face-specific mechanisms can be activated when it is necessary to make detailed discriminations between exemplars within a specific category (Gauthier et al., 1999a, 2000b). Furthermore, when a face is recognized as a face, the identity of the face is also identified where as when non-face objects are identified it is common only to identify them with a general category such as car or bird. Therefore, according to this model face recognition calls on domain-general methods for identifying specific exemplars within a category – this is also done when non-face objects are identified (Gauthier et al., 1999a,

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).

Prosopagnosia, also called face blindness, is a neuropsychological condition that refers to impairment in the recognition of faces. Although prosopagnosic patients suffer from other types of recognition impairments (place recognition, car recognition, facial expression of emotion, est.), they experience face recognition problems above or over other types impairments. Prosopagnosia occurs without intellectual, sensory or cognitive impairments; in other words, people with prosopagnosia can still recognize people from non-facial cues. They cannot recognize familiar people by their faces alone, and often use alternative routes to alleviate the effects of this impairment. These routes include

There are several different types of psychological and neurological phenomena that individuals live with every day. One of most interesting conditions that many individuals might not be aware of is prosopagnosia or face blindness. Prosopagnosia is the inability to recognize and distinguish faces. For example, individuals with this condition could have difficulty recognizing their child’s, spouse’s, parent’s, or friend’s face. Many individuals with prosopagnosia will rely on other features to distinguish between other people, such as hair style or color, voices, or posture. As discussed in Chapter 3, prosopagnosia is thought to affect the Fusiform Face Area.

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).

There exist two famous public domain datasets for aging faces: FGNET and MORPH. The FGNET dataset is a relatively small dataset. It consists of 1002 face images of 82 different persons. The MORPH database was collected over a span of 5 years with numerous images of the same subject. This is not a controlled collection (i.e., it was collected in real-world conditions). MORPH dataset has two versions: Album 1 and Album 2. The MORPH Album 1 contains 1690 face images from 625 different persons. MORPH Album 2 is a database of mugshot images, with associated metadata giving the age, ethnicity, and gender of each subject in the database. Album 2 contain 78, 000 images from 20, 000 different people with each person having at least two cross-age face

Tanaka and Farah (1993) predicted that face recognition is based on their overall shape. Predicting one part of the face would more easily predictable in the whole face than as an isolated part.

1988: Kirby and Sirovich marked the milestone by require no more than a hundred values to calculate the position of faces. They accomplished this by appling principle componet analysis(a linear algebra technique) to the facial recognition problem.

Facial acknowledgment programming is a PC construct program that utilizations focuses in light of still pictures and video pictures on facial components to distinguish a man. It was produced in th 1960s, and is the main semi-robotized framework for facial acknowledgment that required the chairman to find facial elements on photos before it computed separations and proportions to a typical reference indicate that was looked at reference information. (FBI.gov., n.d.) The product works off two methodologies; geometric and photometric; geometric is based off of elements and photometric depends on perspective. Out of the diffrent calculations that were produced, the three, Principal Components Analysis( PCA), Linear Discriminant Analysis (LDA),

In this essay descriptions as well as identifications of research evidence for both Bruce and Young’s 1986 cognitive model of face recognition and Burton, Bruce and Johnston’s 1990 cognitive model of face recognition; which are then applied to two separate case studies and an explanation of which better suits each shall be given. For the purposes of this essay, Bruce and Young’s 1986 model shall be theory 1, and Burton, Bruce and Johnston’s 1990 model, shall be theory 2.

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

Face Recognition is a technology to extract facial features by computer and a technique for authentication according to the characteristics of these features. Face Recognition