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Face Recognition: The Design of Hybrid Solutions

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  1. Introduction: –

In general, any Hybrid solutions used resources in optimal manner so ; many of these resources have hard in executed stage that caused why the system needs more experience for direction in design and codification.

The intercrossed solution intend two or more methods or parts combined together to execute the one solution without go-betweens or integratings merely suited ways to cover with each portion for solution without any job.

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  1. The Detailed Design of the Hybrid Solution Modules: –

As any system the Hybrid solution have many of faculties and sub faculties.

The chief faculties of this system as the followers: –

  1. Capture Images

Any systems need input to pull out end product so, our solutions need input such as image to make some procedure to observe if it can acknowledge as face or non.

  1. Filters and Resize

To make some procedure in this image must has specific size defined by our system and use some filters on it to increase the quality and opportunity for work in good state of affairss.

  1. Face sensing

This portion is working in GPU to upload the image to the GPU so use Haar Cascadess algorithms to observe the face dimensions.

  1. Face acknowledgment

This portion is working in CPU portion to pull out characteristics face so compare with other to happen the existent face.

  1. Tools for design and Execution: –

The inside informations are for these tools as shown in fig ( 4.1 ) .

  1. Windows Form Microsoft.Net C # 2010

Microsoft Visual Studio is an Integrated Development Environment ( IDE ) from Microsoft. It can be used to develop console and graphical user interface applications along with Windows Forms applications, web sites, web applications, and web services in both native codification together with managed codification for all platforms supported by Microsoft Windows, Windows Phone, Windows CE, .NET Framework, .

NET Compact Framework and Microsoft Silverlight.

  1. C #

C # is pronounced “see sharp” . C # is an object-oriented scheduling linguistic communication and portion of the.NET household from Microsoft.

The most recent version is C # 4.0 and it is portion of Visual Studio 2010. C # is really similar to C++ and Java. C # is developed by Microsoft and works merely on the Windows platform.

  1. .NET Model

The.NET Framework ( pronounced “dot net” ) is a package model that runs chiefly on Microsoft Windows. It includes a big library and supports several scheduling linguistic communications which allow linguistic communication interoperability ( each linguistic communication can utilize codification written in other linguistic communications ) .

The.NET library is available to all the scheduling languages that.NET supports. Programs written for the.

NET Framework execute in a package environment, known as the Common Language Runtime ( CLR ) , an application practical machine that provides of import services such as security, memory direction, and exclusion handling. The category library and the CLR together constitute the.NET Framework.

  1. Open CV 2.


OpenCV is released under a BSD licence and hence it’s free for both academic and commercial usage. It has C++ , C, Python and Java interfaces and supports Windows, Linux, Mac OS, Io and Android. OpenCV was designed for computational efficiency and with a strong focal point on real-time applications.

Written in optimized C/C++ , the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogenous compute platform. Adopted all around the universe, OpenCV has more than 47 1000 people of user community and estimated figure of downloads transcending 7 million. Use ranges from synergistic art, to mines review, sewing maps on the web or through advanced robotics.

  1. Emgucv-windows-universal-cuda2.9.0.1922.

    Emgu CV is a cross platform.Net negligee to the OpenCV image processing library. Leting OpenCV maps to be called from.NET compatible linguistic communications such as C # , VB, VC++ , Iron-Python etc.

    The negligee can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices.


CUDA™ is a parallel computer science platform and programming theoretical account that enables dramatic additions in calculating public presentation by tackling the power of the artworks treating unit ( GPU ) .Since its debut in 2006, CUDA has been widely deployed through 1000s of applications and published research documents, and supported by an installed base of over 300 million CUDA-enabled GPUs in notebooks, workstations, compute bunchs and supercomputers.

Learn more about GPU-accelerated applications available for uranology, biological science, chemical science, natural philosophies, informations excavation, fabrication, finance, and more on the package solutions page.Software developers, scientists and research workers can add support for GPU acceleration in their ain applications utilizing one of three simple attacks:Drop in a GPU-accelerated library to replace or augment CPU-only libraries such as MKL BLAS, IPP, FFTW and other widely-used librariesAutomatically parallelize cringles in FORTRAN or C codification utilizing opencut directives for gas pedalsDevelop usage analogue algorithms and libraries utilizing a familiar scheduling linguistic communication such as C, C++ , C # , FORTRAN, Java, Python, etc.

  • Concepts of Algorithms
  • Algorithm is method that can be used by a computing machine for the solution of a job.

    The chief job in Face Recognition is the velocity factor so ; we focus in this issue as a major job in our thesis and we design the algorithm as the followers: –

    1. Main Design:

    png” alt=”” />Figure ( 4.2 ) :Flow chart of intercrossed algorithm

    • As shown in the fig ( 4.2 ) above, we execute intercrossed solution by utilizing GPU and CPU at the sequence to let go of some operating expense on CPU by sharing GPU in this procedure.
    • In other manus, we apply parallel solution in CPU work in portion of face acknowledgments by lading train face parallel ways in the Random Access Memory ( RAM ) and extract characteristics from there earlier go to happen face stage to compare all characteristics from all train faces with the new face to observe if this face is unknown or defined before.

      ( See chapter 3 to see parallel solution in our thesis ) .

      1. Activity flow in algorithm design:
    • Activity diagrams are graphical representations of work flows of stepwise activities and actions with support for pick, loop and concurrence. In the Unified Modeling Language, activity diagrams are intended to pattern both computational and organisational procedures ( i.e.

      work flows ) . Activity diagrams show the overall flow of control.

    • Activity diagrams are constructed from a limited figure of forms, connected with pointers
    • In our algorithm the chief activity is in acknowledgment stage and the other 1 is train stage.
    1. In train face theoretical account

  • GPU portion ( Face sensing )
  • Open new electronic image in GPU memory P-GPUSend P ïƒ P-GPULoad Haar cascades XML and objects ïƒ HHïƒ detect ( P-GPU )Create new CPU electronic image P_CPUSend H.result ïƒ P_CPUCalculate clipAdd log

    1. CPU portion ( Face salvaging )

    If ( P_CPU non = empty ) soP_gray ïƒ P_CPU. grayscaleTake face name from GUI P_nameCreate image file P_fileP_file ïƒ P_grayP_file.name = P_name + random NumebrSave P_file in Hard DiskCalculate clipAdd log

    1. In face acknowledgment theoretical account

    Figure ( 4.4 ) : Activity of acknowledgment stage in intercrossed algorithm

    MeasureNitrogenUmber ANutmeg StateIVITIEs
    1 GUI sends Image from booklet to get down faculty work
    2 Image resizing in fix size determine by admin so use in some image treating filters to increase image quality.
    3 New Image goes to GPU to use haar Cascadess algorithms to observe the face and take other parts of image and return to CPU.
    4 Load train image from DB and extract characteristics for them in parallel manner so new face return to CPU to pull out face characteristics and comparison with other faces in DB.
    5 If find face from DB most near with new face print name in the GUI else print unknown

    Table ( 4.2 ) : Activity Table of train stage in intercrossed algorithmPseudo codification for acknowledgment stage

    1. Low-level formatting

    Selecthow get imageunfastened file duologue to choose imageCreate Bitmap ïƒ PPut new image in PSend P, image-viewer breadth, tallness and quality=72 to Resize new image and put its quality 72Calculate clipAdd log

    1. GPU portion ( Face sensing )

    Open new electronic image in GPU memory P-GPUSend P ïƒ P-GPULoad Haar cascades XML and objects ïƒ HHïƒ detect ( P-GPU )Create new CPU electronic image P_CPUSend H.result ïƒ P_CPUCalculate clipAdd log

    1. CPU portion ( Face acknowledgment )

    Create object from faceReco category P_RecoIf ( P_CPU non = empty ) soOpen Parallel Case{P_Reco burden All Train FaceP_Reco Extract features face for All Train Face}P_Reco find P_CPUExtract P_CPU characteristicsCompare all characteristics faces with P_CPU characteristicsIf find = truePrint P_CPU.name on screenElse Print “Unknown” on screenCalculate clipAdd log

    Cite this Face Recognition: The Design of Hybrid Solutions

    Face Recognition: The Design of Hybrid Solutions. (2017, Jul 22). Retrieved from https://graduateway.com/face-recognition-design-hybrid-solutions-331/

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