We present a face-recognition system based on the optical measurement of linear features. We describe a polarization-based optical system that computes linear projections of an incident irradiance distribution. We quantify the fundamental limitations of optical feature measurement. We find that higher feature fidelity can be obtained by feature-specific imaging than by postprocessing a conventional image. We present feature-fidelity results for wavelet, principal component, and Fisher features. We study face recognition by using a k-nearest neighbors classifier and two different feed-forward neural networks. Each image block is reduced to either a one- or a two-dimensional feature space for input to these recognition algorithms. As high as 99% recognition has been achieved with one-dimensional wavelet feature projections and 100% has been achieved with two-dimensional projections. A 95-fold increase in noise tolerance by use of feature-specific imaging has been demonstrated for an example of the face-recognition problem. An optical experiment is performed to validate these results.
We analyze a novel multispectral imager that directly measures the principal component features of an object. Optical feature extraction is studied for color face images, multi-spectral LANDSAT-7 images, and their grayscale equivalents. Blockwise feature extraction is performed that exploits both spatial and spectral correlation, with the goal of enhancing feature fidelity (i.e., root mean square error). The effect of varying block size, number of features, and detector noise is studied in order to quantify feature fidelity and optimize reconstruction performance. These results are compared with conventional imaging and demonstrate the advantages of the multiplexed approach. Specifically, we find that in addition to reducing the number of detectors within the imager, the reconstruction fidelity (i.e., root mean square error) can be significantly improved using a feature-specific imager.
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