Human face recognition has drawn considerable attention from the researchers in recent years. An automatic face recognition system will find many applications in areas such as human-computer interfaces, model-based video coding and security control systems. A facial recognition system is a computer application for automatically identifying or verifying a person from adigital image or a video frame from a video source.It is typically used in security systems. In this work I used EmguCV cross platform .Net wrapper to the OpenCV image processing library and C# .Net, these library’s allow me capture and process image of a capture device in real time. The main goal of this project is show and explains the easiest way how implement a face detector and recognizer in real time for multiple persons using Principal Component Analysis (PCA) with eigenface for implement it in multiple fields. I used mathematical and matricial techniques, these get the image in raster mode(digital format) and then process and compare pixel by pixel using different methods for obtain a faster and reliable results, obviously these results depend of the machine use to process this due to the huge computational power that these algorithms, functions and routines requires, these are the most popular techniques used for solve this modern problem
Existing System Neural network
Neural network is used to create the face database and recognize the face.A separate network is built for each person. The input face is projected onto the eigenface space first and gets a new descriptor.
Neural networks cannot be retrained. If you add data later, this is almost impossible to add to an existing network.Handling of time series data in neural networks is a very complicated. They require a large diversity of training for real-world operation.
FERET (face recognition technology)
2D recognition is affected by changes in lighting, the person’s hair, the age, and if the person wear glasses.
LDA Linear discriminant analysis
Over fitting problem when performing facerecognition on a large face dataset but with very few training face images available per class. The Problem of Overfitting Data -Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. In order to avoid overfitting, it is necessary to use additional techniques
e.g. cross-validation, regularization, pruning.
In Proposed System we used Principal Component Analysis (PCA) with eigenface; PCA is first applied to the data set to reduce its dimensionality.Find bases which has high variance in data.
The pruning algorithm is a technique used in digital image processing based on mathematical morphology. It is used as a complement to the skeleton and thinning algorithms to remove unwanted parasitic components.
EmguCV Net wrapper to the OpenCV image processing library and C# .Net
Eigen faces for recognition” focused on detecting individual facial features and categorizing different faces by the position, size, and relationship of these features.
PCA- do multiple comparisons and matches between a face detected and the trained images stored in binary database for this reason And for improve the accurate of recognition you should add several images of the same person in different angles, positions and luminance conditions, this training do this prototype solid and very accurate.
This system performs face recognition in real time and also uses this method along with motion cues to segment faces out of images by discarding squares that are classified as non-face images.