Face Recognition Of Java Environment

The digital image of the face of a person can be matched against a record of other images by using face recognition software. In case that some of the images in the database go with the digitized image, the owner is reported about it by the system. Automatic face recognition has been studied and investigated extensively since 1990s and its usage is turning out to be no-nonsense in the present times. Although a number of automatic face recognition applications are relatively agreeable and harmless (access regulation to armaments, currency, illegal proof, nuclear equipment/supplies etc), there are still several dangers of face recognition systems that include violation of privacy and civil liberties, unrestrained exploitation, identity theft, illegal use of database, free access to personal data on social media etc. (Agre, 2001).

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.

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.

face detection: this aims at finding faces in images (or indicate if a face is present)

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,

Abstract: -Face recognition system presents a challenging problem in the field of image processing and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Many researches in face recognition have been dealing with the challenge of the great variability in head pose, lighting intensity and direction, facial expression, and aging. One of them is Identical twin face recognition is a challenging task due to the existence of a high degree of correlation in overall facial appearance. The purpose Identical twin face recognition is mainly focus in the area of security. In this paper we can compare the techniques to identify the twins and techniques are facial aspects and facial marks.

Occlusion the performance of the face recognition algorithms under occlusion is in general poor. The face may be occluded by other objects in the scene or by sunglasses or other things. Occlusion may be unintentional or intentional. Under some conditions subjects may be motivated to thwart recognition efforts by covering portions of their face. Since in many situations, the goal is to recognize none or even un-cooperating subjects.

Using the human face as a key to security, biometric face recognition technology has received significant attention in the past several years due to its potential for a wide variety of applications in both law enforcement and non-law enforcement. As compared with other biometrics systems using fingerprint /palm print and iris, face recognition has distinct advantages because of its non-contact process. Face images can be captured from a distance without touching the person being identified. The identification does not require interacting with the person. Face recognition involves an evaluation of facial features. It identifies and verifies an individual from an digital

With the increase in public surveillance by governments and big coperations, there has grown a need to combat it and its effects on society today. It’s mainly a question of privacy to everyone, can we really trust him, so what’s Public surveillance and automated face recognition? Public Surveillance is the monitoring of conduct, exercises, or other changing data to influence, oversee, coordinate or protect individuals. This can incorporate the use of Closed Circuit Television (CCTV) or capturing of electronically transmitted data, for example, through phone calls. Besides that, it’s no longer a dusk to dawn activity but 24/7. Automated Face Recognition can be best described as the use of specialised software to identify individuals in digital images or videos using sophisticated algorithms that are linked to databases. This kind of software is able to compare several facial features of a person and uniquely identify them amongst a million others accurately.

[3] Zainab A. Othman & Abeer N. Faisal, Face Recognition Model for Age Invariant, Journal of Basrah Researches ((Sciences)) Vol. (41). No. (1) A (2015)

Abstract—Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a technique for human face recognition. A self-organizing program is used to identify if the subject in the input image is “present” or “not present” in the image database. Face recognition with Eigen values is carried out by classifying Eigen values in both images. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed, accuracy and memory utilization. The goal is to implement the system for a particular face and distinguish it

Face Images convey a significant amount of knowledge including information about identity, emotional state, ethnic origin, gender, age, and head orientation of a person shown in face image. This type of information plays a significant role during face-to-face communication between humans [1]. Above prospects of facial images can be used in emerging branch of Human Computer Interaction (HCI). Human age has following characteristics:

The below fig.4 shows the Excepted result of the perfeormance of calculating precision and recall for better annotaion of faces into videos improvement of processing time.It will help to search engine for faster search of videos.

Abstract: In this paper a hybrid technique is used for determining the face from an image. Face detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP) formula, which aims at finding a linear approximation to the Eigen functions of the astronomer Beltrami operator on the face manifold. However, LPP is non-orthogonal and this makes it difficult to reconstruct the information. When the set of features is found by the OLPP, with the help of the PSO, the grouping of the image features is done and the one with the best match from the database is given as the result. This hybrid technique gives a higher accuracy in less processing time.

As a scientific discipline, computer vision is concerned with the extraction of information from images to be employed in a decision making process. The image data can take many forms, such as video sequences, still images, digitized maps, diagrams and sketches. These images may be in colour format, grey scale or in binary format. The common approach is to extract characteristic features from the image either in the spatial domain or in some suitable transform domain. Whether the goal is classification or recognition, a measure of similarity or distance must be formulated and the success rate of the system evaluated. Some of the application areas of computer vision are: