Facial Recognition Essay

The one feature that got the most attention and strongly influenced its demise was its handwriting recognition capability. In all message pads, handwriting recognition was the basis of data input to many of the built-in applications and functions. According to Professor Luckie, this handwriting recognition depends solely on Paragraph International Inc.’s Calligrapher recognition engine. Calligrapher technology was limited by the dictionary of words to which it has access (Luckie, n.d.). This shows that if you write a word that doesn’t exist in the dictionary, then Calligrapher is not going to recognize it correctly and often suggests funny but incorrect phrases as the user wrote.

In effect, people are unknowingly being put in suspect lineups without their awareness or consent. This can lead to false accusations against innocent citizens. Most police departments still rely on officers to verify that the suspect chosen by the face recognition software actually matches the camera footage. However, humans shockingly make an error in this process once in every two cases (Bedoya, Alvaro, et al 2016). In some instances, it is not only this human error that is leading to their conviction but rather the system itself. A study, co-authored by the FBI, said that the facial recognition software is actually less accurate when it comes to identifying African Americans. Systems relying on mugshot databases have a disproportionate number of African Americans due to their high arrest rates in America. This creates “racial biased error rates,” that perpetuate implicit and systemic racism in our society (Bedoya, Alvaro, et al 2016). But for American citizens in general, in any crime being solved by using face databases, anyone is a potential suspect. Because of this, regulations should be put in place to limit the use of this technology to cases where its use is relevant and vital to solving the issue.

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.

Like acne, for example, most people have them for months, while others may just have them just for the week, but the recognition system has already picked up that individual's unique facial patterns and marks. The use of facial recognition in a video is of great value for the police departments because it reinforces the use of security cameras as admissible evidence in courtroom cases. Facial recognition could even identify an individual even if they are not facing the camera directly or in frontal view. In a normal courtroom scenario for robbery caught on video, if the robber solely wore shades and just a hoodie, the evidence presented using facial recognition would stand admissible in court. There have been many non-believers in regards to

1966: The First Facial recognition systemdeveloped in 1966 helped administrator identify features on photographs like ears, mouth and nose to calculate ratios and distances to a reference point.

The process of making an identification based off the method of facial recognition is far more advanced. Facial recognition measures distinguishing facial features such as the dimensions of the nose, the distance between eye sockets, the slope of the forehead, the line and size of the jaw, and the contours of the cheeks (Dewey et al., 2013). Once the subjects photograph is obtained by the specific system, it uses the above parameters to compare the subjects photograph to one which had been previously taken and stored in the organizations

Facial recognition has been widely used for identifying terriorist and criminals. The U.S. government has recently begun a program called US-VISIT (United States Visitor and Immigrant Status Indicator Technology), aimed at foreign travelers gaining entry to the United States. (4)When a foreigners applies for his/her visa in US Ambassy, he/she will submit fingerprints and have his/her photograph taken. The fingerprints and photograph will then be compared with the exsiting database and to be determined if they match the records of exsiting criminals and terrorists.

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),

You already mentioned some very interesting examples of where face recognition could be used or already is in use. Authentication is needed more and more in a digitalized world. At first, I thought face recognition could be useful for unlocking phones or open door locks, for example. But I came to notice that there are situations in which this face recognition could actually be disturbing or even dangerous. Imagine a person having an accident and somebody tries to call for help using the cell phone of this person. Help could be hindered by a phone only unlocking through face recognition of the owner. A similar situation would arise in a burning house. The owner being trapped inside, firefighters may not be able to enter the house for help as quickly as they could without a face recognition lock.

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

A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the challenges in face recognition is to come up with a matching model that is robust to effects and changes of facial aging. Aging leads to both shape and textural changes in the face of a person. They(shape and texture) offer important features for facial image classification related to the same individual and also distinguishing between face images of different individuals.

Face recognition under extreme facial expression still remains an unsolved problem, and temporal information can provide significant additional information in face recognition under expression. A neutral face is a relaxed face without contraction of facial muscles and without facial movements. Face recognition systems can achieve high recognition rate for good quality, frontal view, constant lighting and only subtle expression or expressionless face images.Therefore, it is important to automatically find the best face of a subject from the images. Using the neutral face during enrolment and when authenticating, so that we can find the neutral face of the subject from the six universal expression like. Happiness, sadness, disgust, anger, fear, surprise.

Today facial recognition has a high success rate. It is very difficult to fool the system, so you can feel secure knowing that your biometrics computer security system will be successful at tracking time and attendance while providing better security.

[1] Amal Seralkhatem Osman Ali & Hassan Ameen, Age Invariant Face Recognition System using combined Shape and Texture Features,IET Biom.,2015,vol4,ISS2,PP.98-115.

Haitao Zhao, Pong Chi Yuen says face recognition has been an active research area in the computer-vision and pattern-recognition societies [11] in the last two decades. Since the original input-image space has a very high dimension, a dimensionality-reduction technique is usually employed before classification takes place. Principal component analysis (PCA) is one of the most popular representation methods for face recognition. It does not only reduce the image dimension, but also provides a compact feature for representing a face image. In 1997, PCA was also employed for dimension reduction for linear discriminant . PCA is