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Face images are an important source of information for biometric recognition and intelligence gathering. While face
recognition research has made significant progress over the past few decades, recognition of faces at extended ranges is
still highly problematic. Recognition of a low-resolution probe face image from a gallery database, typically containing
high resolution facial imagery, leads to lowered performance than traditional face recognition techniques. Learning and
super-resolution based approaches have been proposed to improve face recognition at extended ranges; however, the
resolution threshold for face recognition has not been examined extensively. Establishing a threshold resolution
corresponding to the theoretical and empirical limitations of low resolution face recognition will allow algorithm
developers to avoid focusing on improving performance where no distinguishable information for identification exists in
the acquired signal. This work examines the intrinsic dimensionality of facial signatures and seeks to estimate a lower
bound for the size of a face image required for recognition. We estimate a lower bound for face signatures in the visible
and thermal spectra by conducting eigenanalysis using principal component analysis (PCA) (i.e., eigenfaces approach).
We seek to estimate the intrinsic dimensionality of facial signatures, in terms of reconstruction error, by maximizing the
amount of variance retained in the reconstructed dataset while minimizing the number of reconstruction components.
Extending on this approach, we also examine the identification error to estimate the dimensionality lower bound for low-resolution
to high-resolution (LR-to-HR) face recognition performance. Two multimodal face datasets are used for this
study to evaluate the effects of dataset size and diversity on the underlying intrinsic dimensionality: 1) 50-subject
NVESD face dataset (containing visible, MWIR, LWIR face imagery) and 2) 119-subject WSRI face dataset (containing
visible and MWIR face imagery).
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