Evaluation of wavelet based linear subspace techniques for face recognition

Face recognition is a complex visual classification task which plays an important role in computer vision, image processing and pattern recognition. Research concerning the face recognition started nearly in 1960s. The initial research directions in 1960’s are based on locating the features such as eyes, ears, nose and mouth on the photograph and calculating the distance between the reference image and the stored images. In 1990’s, researchers introduced linear subspace techniques, statistics related techniques, to face recognition problems. The introduction of the linear subspace techniques is a milestone in the face recognition concept. The main objective of the thesis is to improve the recognition rate of existing face recognition methods for large databases with varying pose, expression and environmental conditions (lighting, rainy etc). The human skill of identifying thousands of people even after so many years with different aging, different light conditions and viewing conditions excited many researchers to focus in face recognition systems. Researchers have developed various biometric techniques to identify or recognize persons with their physical characteristics like finger, voice, face etc. These biometric techniques have their own advantages and drawbacks as well. Among all the biometric techniques face recognition has a distinct advantage of collecting the required data or image without individual cooperation. In this work, we are attempting to answer the following research questions: • Is the face recognition system invariant to face expressions? • Is the face recognition system invariant to environmental conditions (like background, climate changes)? • Is the face recognition system invariant to viewing conditions? • Is the face recognition system giving higher performance or high recognition rate for large databases? • Is the wavelet based linear subspace technique a good alternative to perform face recognition tasks? • What is the efficiency of the wavelet based linear subspace technique when compared with its counter parts? In this thesis, different linear subspace techniques like principal component analysis (PCA), independent component analysis (ICA) and linear discriminant analysis (LDA)

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