The paper presents a simple, original method, using supervised learning and pattern matching, for frontal-view face detection. The raw method and algorithms, their refinements and optimizations, the experimental system, and the obtained results are described incrementally. A pyramid of several simplified representations for faces, with gradual complexity and dimensionality from coarser to more detailed ones, has been defined. These representations were used as a basis for structuring and organizing the knowledge base as a kind of hash tree, as well as to optimize the developed algorithms and their processing time, by applying a sequence of filters with gradual computational complexity in cascade at detection. Appropriate metrics have been defined and used to evaluate the similarity between two finite sets of binary values, of the same dimension, with a minimum of computational effort in these filters. Various thresholds of passage were empirically chosen and adjusted. An experimental system was developed and used for learning and detection tests. Public image databases and private images have been employed, and quite promising results have been obtained. Finally, comparative parallels with some reference methods are discussed.
face detection, supervised learning, positive / negative examples, hash code, clusterization, pattern matching, similarity score, cascade of filters.
Mihnea Horia VREJOIU, "A Pattern Matching Method and Algorithm for Face Detection", Studies in Informatics and Control, ISSN 1220-1766, vol. 26(1), pp. 75-86, 2017. https://doi.org/10.24846/v26i1y201709
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