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Face Recognition, Feature Space, Wavelet Analysis, HOG Descriptors, Principal Component Analysis, Feature Fusion, Classifier Space, Classifier Ensemble, Linear Discriminant Classifiers, Robust Classification, Cross Validation.

Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.