The Dangers of Facial Recognition Technology

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.

The article "A Look into Facebook's Potential To Recognize Anybody's Face” by Martin Kaste focuses on one main topic: facial recognition software. Facial recognition software may seem very complicated, but it’s actually where a computer can tell your identity from simply looking at your face in a picture. Your face is like your fingerprint: It’s a set of identifying markers that are distinct to you and only you, and it can’t be erased. The FBI and others have amassed mass databases of faces, but Facebook has the biggest, and Facebook is best at using them.. According to the article, Facebook can be used to assemble multiple photos of a person to get an accurate model. Although, this raises a question about how the government actually uses social media to

Marin Kaste wrote the article “A Look Into Facebook’s Potential To Recognize Anybody’s Face.” This article discusses the challenge of using facial recognition software to determine an individual’s name. The software used for facial recognition has numerous defects, which causes problems when trying to identify an individual using a poor quality photo. Another problem with facial recognition software is if an individual is not in the database, then they cannot be found using the software. The article explains that the problems of facial recognition could be solved by collecting all of the photos of individuals on Facebook to create a universal database.

Face recognition has attracted great attention in recent years. An important application of face recognition is to assist law enforcement. For example, Uhl and lobo [1] proposed the first automatic retrieval of photos using a query sketch based on face recognition algorithm and more recently Klare and jain [2] suggests an effective method of matching sketch photo using many technique such as local descriptors similarities algorithm .Once this forensic sketch is ready, it is disseminated to law enforcement officers and media outlets hoping that someone might be knowing the suspect. The first approaches developed by Tang and Wang (2002, 2003, 2004) [3] use global linear transformations, based on eigenface method to convert

We have made a scanner in which it scans the face and looks your facial features. Depending on how many features match with the picture obtained you will be granted access to the area you wish to go to. Facial Recognition is a kind of security measure that will be used in the future in order to grant access into places or computer programs. Facial Recognition is and will not only be used to access places or computer but it is also currently being used to gather demographic data on crowds. In some cases facial recognition is already being used to open banking accounts.

To perform facial recognition tasks, the accuracy of facial point detectors used is of the utmost importance but since facial features vary, it is important to validate facial point detectors across different facial expressions. \cite{NEEDED}\cmt{[1152]}, \cite{NEEDED}\cmt{[1162]}.

Age related face recognition is an important and challinging problem which received much prominence during recent years, especially for real life applications of face recognition systems in which age compensation is required, like as for identifying missing individus, conducting surveillance or verifying passport. where subjects are either not available or are trying to hide their identity. In such applications, there may be a significant age difference between the query image and those stored in the database, and it may be impossible to obtain the subject’s recent face images to update the database. Developing age-invariant face recognition systems allows accurate identification even in the presence of age variations and would avoid the necessity

The idea of using someone’s body as a form of their identification has been a concept put to use for thousands of years. Although, since the incorporation of biometrics - the measurement and analysis of unique physical or behavioral characteristics (Merriam-Webster, 1902) - in the early 1900’s, the primary focus has been to advance private security and accessibility. When first introduced, biometrics were used in the public sector of the economy to help identify people, in ways such as: health care, police investigation, military usage, etc. Within recent years, the demand for biometrics, due to its attractiveness in the security field, not only led to a shift in economic sectors but to an overall expansion in the biometric companies; as well as, lead to an increase in their gross income. And with a shift in economic sectors and a rise in revenue, the debate is whether the pros will outweigh the cons, and if biometrics is the most beneficial outcome for the country at whole.

Face recognition is the authentication by analysing specific features that are common to everyone 's face. These features can be the distance between the customers eyes, the width of customers the nose, the position of

Facial recognition isn’t simply just recognizing a face and knowing who that person is. We perceive faces as whole not as a collection of different features. It can be done in three steps; 1. presence of the face is noticed, 2. The face is then measured to find

In this report, we are considering digital facial images. We will discuss the various existing techniques for extracting shape and texture features from a facial image and also techniques of fusing these two feature sets together to obtain a higher accuracy. We will also review the existing age-invariant facial recognition systems, their accuracies and limitations.

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.

addressed before it is widely used in public [5]. Public concern about privacy issues with

[4] Sonu Agrawal, Sushil Kumar, A Novel Idea for Age Invariant Face Recognition, International Journal of Innovative Research in Science, Engineering and Technology

Recently, appearance-based face recognition has received tons of attention. In general, a face image of size n1 × n2 is delineating as a vector within the image house Rn1 × n2. We have a tendency to denote