I'm looking for image recognition by using texture feature of matlab.

face recognition with rangefilt


ABSTRACT

The human brain exhibits robustness against natural variability occurring in face images, yet the commonly attempted algorithms for face recognition are not modular and do not apply the principle of binary decisions made by the firing of neurons. We present a biologically inspired modular unit implemented as an algorithm for face recognition that applies pixel-wise local binary decisions on similarity of spatial-intensity change features. The results obtained with a single gallery image per person show a robust and high recognition performance: 94% on AR, 98% on Yale, 97% on ORL, 97% on FERET (fb), 92% on FERET (fc), and 96% on Caltech face image databases.

OpenCV is an open-source computer vision and machine learning library. It contains thousands of optimized algorithms, which provide a common toolkit for various computer vision applications. According to the project’s about page, OpenCV is being used in many applications, ranging from stitching Google’s Street View images to running interactive art shows.OpenCV started out as a research project inside Intel in 1999. It has been in active development since then, and evolved to support modern technologies like OpenCL and OpenGL and platforms like iOS and Android.In 1999, Half-Life was released and became extremely popular. Intel Pentium 3 was the state-of-the-art CPU, and 400-500MHz clock speeds were considered fast. And a typical CPU in 2006 when OpenCV 1.0 was released had about the same CPU performance as the A6 in an iPhone 5. Even though computer vision is traditionally considered to be a computationally intensive application, clearly our mobile devices have already passed the threshold of being able to perform useful computer vision tasks, and can be extremely versatile computer vision platforms with their attached cameras.In this article, I will provide an overview of OpenCV from an iOS developer’s perspective and introduce a few fundamental classes and concepts. Additionally, I cover how to integrate OpenCV to your iOS projects, and share the basics of Objective-C++. Finally, we’ll look at a demo project to see how OpenCV can be used on an iOS device to perform facial detection and recognition.

Overview of OpenCV

Concepts

OpenCV is a C++ API consisting of various modules containing a wide range of functions, from low-level image color space conversions to high-level machine learning tools.Using C++ APIs for iOS development is not something most of us do daily. You need to use Objective-C++ for the files calling OpenCV methods, i.e. you cannot call OpenCV methods from Swift or Objective-C. The OpenCV iOS tutorials tell you to simply change the file extensions to .mm for all classes where you’d like to use OpenCV, including view controllers. While this might work, it is not a particularly good idea. The correct approach is to write Objective-C++ wrapper classes for all OpenCV functionality you would like to use in your app. These Objective-C++ wrappers translate OpenCV’s C++ APIs to safe Objective-C APIs and can be used transparently in all Objective-C classes. Going the wrapper route, you will be able to contain C++ code in your project to the wrappers only and most likely save lots of headaches further down the road in resolving hard-to-track compile errors because a C++ header was erroneously included in a wrong file.OpenCV declares the cv namespace, such that classes are prefixed with cv::, like cv::Mat, cv::Algorithm, etc. It is possible to use using namespace cv in your .mm files in order to be able to drop the cv:: prefixes for a lot of classes, but you will still need to write them out for classes like cv::Rect and cv:oint, due to collisions with Rect and Point defined in MacTypes.h. While it’s a matter of personal preference, I prefer to use cv:: everywhere for the sake of consistency.