Recognition using robust bit extraction

We present a novel technique for extracting bits from the perceptually significant components of an image transformation, thus making the recognition of objects under nonideal conditions robust. Specifically, we describe five popular face recognition transform methods [including principal component analysis (PCA), linear discriminant analysis (LDA), wavelet transform, wavelet transform with PCA, and wavelet transform with Fourier-Mellin transform] with robust bit extraction enhancement for various numbers of bits extracted. The robustness guarantees that all similar face images will produce almost the same bits. This property is useful for generating cryptographic keys. The theoretical results are evaluated on the Olivetti Research Laboratory (ORL) face database, showing that the extended methods significantly outperform the corresponding standard methods when the number of extracted bits reaches 100.