Face Recognition Technology


Face recognition

Face Recognition Based on PCA

DCT-ANN Face Identification

Wavelet-ANN Face Recognition

Face Recognition Based on Polar Frequency Features

Face Recognition Based on FisherFaces

Face Recognition Based on Local Features

Face Recognition in Fourier Space

WebCam Face Identification

Face Recognition Based on Overlapping DCT

Face Recognition Based on Statistical Moments

Face Recognition Based on Nonlinear PCA

Face Recognition Based on Hierarchical Dimensionality Reduction

Fusion of Low-Computational Global and Local Features For Face Recognition

SVD-Based Face Recognition

Correlation Filters Face Verification

ICA Face Recognition

3D Face Recognition

Infrared Face Recognition

Octave Face Recognition

PHP Face Recognition

JAVA Face Recognition

LBP Face Recognition System

HMM Face Recognition System

NMF Face Recognition System

Face matching

Face Identification Based on CPD

GA MACE Face Verification

External resources

Advanced Source Code .Com

Neural Networks .It

Genetic Algorithms .It

Iris Recognition .It

Face Recognition with Local Binary Patterns

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox, Matlab Wavelet Toolbox, Matlab Signal Processing Toolbox and Matlab Computer Vision Toolbox.

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.

Release 2.0 Date 2013.12.03
Major features:
Release 1.0 Date 2013.09.08
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

Face Recognition . It Luigi Rosa mobile +39 3207214179 luigi.rosa@tiscali.it