Reduced-complexity biometric recognition using 1-D cross-sections of correlation filters

Correlation filters are an attractive image processing technique for object recognition. They can provide the necessary recognition accuracy for many applications, but it would be desirable to reduce the complexity of the correlation filter algorithm (in terms of computation and storage space). This is especially true for biometric identification tasks, where multiple correlation filters must be tested against a single image. We propose an algorithm for match metric computation that trades a (usually minor) degradation in accuracy for an orders-of-magnitude complexity reduction. This algorithm analyzes ID cross-sections of the frequency domain in which the filter is applied. We compare our proposed technique to the standard technique using a dataset of face images.