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After a frequency-domain-preprocessing of any standard 2D images, the standard patterns to be learned can generally be presented by N-D analog vectors. When these vectors satisfy the most general separability condition, the positive linear independency (or PLI) condition, these vectors can definitely be learned by a one-layered, hard-limited perceptron in a noniterative manner. From this noniterative learning scheme, an automatic feature extraction and automatic feature competition method can be derived which will enhance significantly the robustness in the recognition mode. This paper reports the theoretical analysis of this novel feature extraction scheme and the experimental result demonstrating the robustness of recognition and the speed of learning of this scheme. Comparison of this novel feature extraction scheme with Karhunen-Loeve scheme and the SVD (singular value decomposition) scheme is discussed in detail.
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