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A novel genetic algorithm (GA) is proposed for searching for the existence of a projective transform which, when applied to the model, results in the best alignment with an unknown 2D edge image. The presence of a valid solution reflects that the latter can be regarded as one of the projected views of the model. On this basis, the identity of an unknown edge image can be deduced by matching it against a set of 3D reference models. To increase the efficiency of the process, a two-pass, coarse-to-fine strategy is adopted. Initially, an unknown image is first classified to a small group of models by matching their outermost boundaries. Next, a fine but slower matching algorithm selects model(s) that share similar internal edge features as the unknown image. In the design of the method, the authors have adopted an alternative projective transform representation that involves fewer parameters and allows constraints to be easily imposed on their dynamic ranges. This effectively lowers the chance of premature convergence and increases the success rate. Experimental results obtained with the proposed scheme are encouraging and demonstrate the feasibility of the approach.