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Coding Facial Expression with Oriented Steerable Filters

In this paper, facial expression is coded by steerable filters which are rotated very efficiently by taking a suitable linear combination of basis filters. Local features extracted by steerable filters are locally stable with respect to scale, noise, and brightness changes, and distinctive enough to capture subtle facial expression cues. Further more, steerable filters are implemented within a Gaussian pyramid to exploit discriminative power in scale-space. Responses of the filters are concatenated to an augmented feature vector to evaluate the similarity between different facial expression images with the nearest-neighbor rule for final decisions. In comparison with Gabor filters, steerable filters save much computational cost and obtain comparable recognition performance with fewer features. Experiments on the JAFFE database demonstrate the effectiveness of steerable filters for coding facial expression.