Face Detection and Recognition

Datta, Anuja; Datta, Madhura; Banerjee, Pradipta

doi:10.1201/b19349

Cited by 30 publications

(12 citation statements)

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“…(1) The database is fully replicated among servers. (2) The data is characterized as rapidly changing [3]; users oft>en query servers to remain up-todate. More specifically, they will often want to track every broadcast for their data item of interest.…”

Section: Introductionmentioning

confidence: 99%

Adaptive broadcast protocols to support power conservant retrieval by mobile users

Datta

Celik²,

Kim³

et al.

Proceedings 13th International Conference on Data Engineering

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Mobile computing has the potential f o r managing information globally. Data management issues in mobile computing have received some attention in recent times, and the design of adaptive broadcast protocols has been posed as an important problem. Such protocols are employed by database servers to decide on the content of broadcasts dynamically, in response to client mobility and demand patterns. In this paper we design such protocols and a1 s o propose e@ci en t retrieval st rat egies that m.ay be employed b y clients to download information from broadcasts. The goal is to design cooperative strategies between server and client to provide access to information in such a way as to mini,mize energy expenditure b y clients. W e evaluate the performance of our protocols analytically.

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Section: Introductionmentioning

confidence: 99%

Adaptive broadcast protocols to support power conservant retrieval by mobile users

Datta

Celik²,

Kim³

et al.

Proceedings 13th International Conference on Data Engineering

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“…(Chityala et al, 2014). The following figure shows the suitability of facial identification in comparison to other biometric methods (Datta et al, 2015). As a biometric system, a system of face recognition operates in two methods: face verification and face identification.…”

Section: Introductionmentioning

confidence: 99%

THE MODELING OF FACIAL RECOGNITION PROCESS IN PROSPECTIVE OF SIMULATION TECHNIQUES (A methodical elaboration through the built-in modules of Matlab)

Bushati¹,

Lesha²,

Strica³

2018

Proceedings of 9th International Symposium on Graphic Engineering and Design

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Biometrics is a field of study and dissemination that brings a host of contributing domains in an integrated way to generate products that find multiple applications particularly in creating an individual's identity. Modeling biometric processes reveals a series of discussions in various fields, mainly in medicine and in the digital image processing field. These discussions range from the simulation levels of these models to those of application and concrete products that use biometric systems for identifying and recognizing individuals. Indeed, over many biological parameters of an individual to be identifiable, there is the face that generates a lot of information about the physical data of the individual. According to specific studies, facial components combined according to different methods can be used to design simulation models that come in aid of generating closed-source and dedicated programs of biometric recognition (Das, 2018). This paper deals with a simulation model, through the Matlab test-bench, of the facial recognition process. The model is created at the proposal level for the subject field and gives the limits and recommendations for improvements and further developments of this simulation method. The modeling methodology includes two parts; the first part is the Matlab program that has been coded in such a way that it takes some images that represent the faces of different people published in the opensource database on the Internet; the second part is the creation of a database that retains these images to enable face identification when entering a new image program. The facial search and retrieval process are built through some built-in functions that the latest versions of Matlab offer. Finally, the simulation model provides a new facial identification model that leaves room for further discussion and limits to the relatively small number of face images that can support the database in Matlab.

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“…First of all there is a need for protecting personal sensitive information Which can be solve using face recognition systems [4]. Generally a password and user name is assigned to service consumers to be authenticated.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore some mission sensitive environments requires biometric authentication such as finger print, personal vain map, retina scan. One of the most widely used biometric authentication method is face recognition in which the photograph of an official ID card is matched with camera snapshot [4]. There are many face detection and recognition methods which can be implemented in software namely Eigenface, Fisherface, LBPHface and SURF algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Implementation and Evaluation of Face Recognition Based Identification System

Elbizim

Kasapbaşi

2017

ijisae

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Face recognition has been widely used and implemented to many systems for the purpose of authentication, identification, finding faces, etc. In this study Yale face database [1] is used which consist of 15 different people. For each of person there are 11 different images with different face expressions. In this study images are categorized as normal, normal and centre light, normal and happy, normal with left light and right light. In order to recognize these faces 4 different face recognition methods namely Eigenface, Fisherface, LBPHface and SURF are utilized in the developed environment. In order to test the mentioned face recognition algorithms a software is developed using EmguCV in .NET environment. After evaluating and comparing the obtained confusion matrix amongst other the LBPHface method was found to be superior method with an average accuracy of 99%, it was ~98% SURF, ~97% for EigenFace and FisherFace. FicherFace was slightly better then the Eigenface method.

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Face Detection and Recognition

Cited by 30 publications

References 0 publications

Adaptive broadcast protocols to support power conservant retrieval by mobile users

Adaptive broadcast protocols to support power conservant retrieval by mobile users

THE MODELING OF FACIAL RECOGNITION PROCESS IN PROSPECTIVE OF SIMULATION TECHNIQUES (A methodical elaboration through the built-in modules of Matlab)

Implementation and Evaluation of Face Recognition Based Identification System

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