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Source code for fingerprint recognition, face recognition and much more

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The local binary pattern (LBP) was originally designed for texture description. It is invariant to monotonic grey-scale transformations which is very important for texture analysis. Also due to the computational simplicity processing of image in real time is possible. With LBP it is possible to describe the texture and shape of a digital image.

We have developed a fast approach for face recognition combining classifiers based on both micro texture in spatial domain provided by local binary pattern and macro information in frequency domain acquired from the discrete cosine transform (DCT) and many other features to represent facial image. We have tested code on 6146 faces, 477 classes (an average number of 13 faces for each class), obtaining an EER equal to 3.07%. The facial database includes 6146 manually cropped 128-by-128 grayscale images. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open/closed eyes, smiling/not smiling, eye blinking, ...) and facial details (glasses/no glasses). Many images were taken against an inhomogeneous background with the subjects in an upright, frontal position, with tolerance for some side movement. Database includes but not limited to most of common face datasets used in pattern recognition, such as AT&T Face Database, JAFFE Database, Yale Database and much more.

Index Terms: Matlab, source, code, LBP, local, binary, pattern, patterns, dct, face, recognition, matching.






Figure 1. Local binary patterns

A simple and effective source code for LBP Face Recognition System.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Wavelet Toolbox, Matlab Signal Processing Toolbox and Matlab Computer Vision Toolbox are required.

Major features




  • Face Recognition based on Local Binary Patterns
  • Efficient dimensionality reduction
  • Automatic face detection
  • Manual face detection for a better face localization
  • Image acquisition from webcam
  • Free updates
  • Fast and optimized implementation
  • Easy and intuitive GUI

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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Wavelet Toolbox, Matlab Signal Processing Toolbox and Matlab Computer Vision Toolbox are required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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