Automatic Face Recognition Essay

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.

Facial Recognition software has been used in many atmospheres to assist in security. There has been controversy as to wither or not facial recognition is an accurate tool. The software has been in existence for many years but still can be defeated by criminals, terrorists, and even by citizens in general without malice.

Face recognition applications are computation intensive applications. They try to extract every small detail from the image provided for accurate face detection and recognition. They are able to accurately recognize the faces with the results being accurate up to 90% [1]. Accuracy of recognized objects is a crucial factor. The accuracy of the recognizer depends on the recognition algorithms implemented. The object needs to be recognized quickly without compromising the level of accuracy [2].

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

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]}.

One of the most accepted theories on face processing involves holistic face perception in which faces are recognized as a conceptual whole, by integration of spatial relations among facial components and featural information into one representation.

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

In the field of cognitive psychology, one of the major debates amongst scholars is the issue of facial recognition. The argument lies on the fact if facial recognition is based on either configural (i.e. the eyes go above the nose) or featural (i.e. mouth, eyes, etc.) processing, or both of these combined. There have been a handful of individuals that have conducted experiments in order to prove if recognition is one over the other or if it is both of them. This debate has managed to proceed for many years and each researcher has produced slightly different results.

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.

The act of recognizing a face is actually quite complex. Faces must be accurately recognized in any condition, even while moving. But unlike other objects, faces are intimately involved in communication, and our brains must be able to extract a tremendous amount of subtle detail from just a glance. So while some of the issues involved in face recognition are the same as for recognizing any object, other issues are unique to faces.

It may be possible to visualise such relationship for two- or three-dimensional problems, yet, a computer algorithm will be needed to optimise the decision boundary in higher-dimensional feature spaces.

This data set has 66 principal constituents to take account of 66 sea bright attributes and 1 classification uses in distinguishing is away from image 50x50 the example production example. A kind has represented the value, contracts the serious facial paralysis face pattern recognition from a person who. -1 representative 's social class values, from a normal person who also non-facial nerve paralysis face pattern recognition.

Abstract—In this world of globalization there are many social networking sits and it contains huge database (images) that are uploaded on it. For mining those images for exact person, we haven’t any way for extracting those images from online social networking, thus the face detection method originate for social media side. In the image processing, face detection is one of the challenging problem yet we have novel methods present to face challenge. Face annotation methods are playing very important role in real world knowledge system such as online photo collection management, new video representation etc. Each method, which is used for auto face detection in real world, has well performance and other side it having limitations also. How to overcome from these limitations? For that a survey has taken and collects those problems. Also this paper addresses various methods that are used to facial image annotation and how to overcome from those limitations. This survey will help in future for image and web community also there are some new open challenging problems for face annotation.

Principle component analysis, also referred to as eigenvector transformation, Hotelling transformation and Karhunen Loeve transformation in remote sensing, is a multivariate technique [66] that is used to decrease dataset dimensionality. In this technique, the original remote sensing dataset, which is a correlated variable, is distorted into a simpler dataset for analysis. This permits the dataset to be uncorrelated variables representing the most significant information from the novel [21]. The computation of the variance covariance matrix (C) of multiband images is expressed as:

Abstract— There is an urgent need to organize and manage images of people automatically due to the recent explosion of such data on the Web in general and in social media in particular. Beyond face detection and face recognition, which have been extensively studied over the past decade, perhaps the most interesting aspect related to human-centered images is the relationship of people in the image. In this work, we focus on a novel solution to the latter problem, in particular the kin relationships. To this end, we constructed two databases: the first one named UB KinFace Ver2.0, which consists of images of children, their young parents and old parents, and the second one named FamilyFace. Next, we develop a transfer subspace learning based algorithm in order to reduce the significant differences in the appearance distributions between children and old parent’s facial images. Moreover, by exploring the semantic relevance of the associated metadata, we propose an algorithm to predict the most likely kin relationships embedded in an image