Improved PCA based Face Recognition using Feature based Classifier Ensemble

Automatic face recognition has been a challenging problem in the field of image processing and has received a great attention over the last few decades because of its applications in different cases. Most of the face recognition systems employ single type of data, such as faces, to classify the unknown subject among many trained subjects. Multimodal systems are also available to improve the recognition performance by combining different types of data such as image and speech for the recognition of the subjects. In this thesis, an alternative approach is used where the given face data is used to automatically generate multiple sub feature data sets such as eyes, nose and mouth. Feature extraction is automatically performed by using rough features regions extracted from Viola-Jones face detector followed by Harris corner detector and Hough Transform for refinement. Automatically generated feature sets are used to train separate classifiers which would recognize a person from its respective feature. Given separate feature classifiers, standard data fusion techniques are used in the form of classifier assembling to improve the performance of the face recognition system. 10-Fold cross validation methodology is used to train and test the performance of the respective classifiers, where nine fold is used for training and one fold is used for testing. Principal component analysis (PCA) is employed as a data dimensionality reduction method in each classifier. Five different classifiers for right and left eyes, nose, mouth and face data sets are developed using PCA. The classifiers, of five different features are merged by different data fusion techniques such as Minimum Distance, Majority Voting, Maximum Probability, Sum and Product Rule. Overall, the proposed algorithm using the Minimum Distance improves the accuracy of state-of the art performance from 97.00% to 99.25% using ORL face database. Keywords: Face Recognition, Face Detection, Cross Validation, Viola-Jones Detection, Feature Extraction, Minimum Distance, Data Fusion, Classifier Ensemble.