Pose and Lighting Invariant Real-Time Face Recognition
An efficient face recognition algorithm based on a modular PCA approach that has an improved recognition rate for large variations in pose, lighting direction and facial expression is developed. In this technique the face images are divided into smaller sub-images and the PCA approach is applied to each of these sub-images. The training phase of the technique begins by extracting the eigenvectors corresponding to the largest Eigenvalues of a covariance matrix, which is constructed from the training image database. These eigenvectors are used for creating a generalized face feature vector, which can classify the incoming face images successfully. Experimental results demonstrate that the modular PCA method has higher recognition rate when compared with the traditional PCA method for tests conducted on the UMIST and Yale databases.
The face recognition system functions by recieving a real-time video stream from a standard surveillance camera and detecting all faces in the image region. These face images are then analyzed and compared with features stored in a database to determine the identity.
Overall system view of a face recognition system.
In order to recognize faces in real-time, the system is trained beforehand using a database of known faces. The training images are input into a Modular PCA training algorithm which calculates generalized feature vectors for each known person.
Face Training Database
Modular PCA Training Block Diagram
After successfully training the system, the real-time recognition process can begin. Video is input to the system and broken into modules just as in the training process. Each module is assigned a weighting value according to the variance of the module. The larger the variance, the greater the weight value. This allows the system to focus on regions with greater detail.
Module Weighting Process
The system then uses the calculated features of the input image to compare with features from the trained database to determine the identity of the input face image.
Comparison of Face Recognition Methods
Demo #1 - Long Range Face Recognition
Demo #2 - Night Vision Face Recognition
Demo #3 - Face Recognition with Kalman Tracker
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