In this article, two key techniques, a multiple filter set and a feature classification method based on support vector machines (SVMs), are proposed for fingerprint feature identification. The Gabor filter (GF) has been verified as a successful method and is usually selected for fingerprint feature detection. Although the GF has high accuracy in feature extraction under a wide angle representation range, not all degree ranges of the filter are necessary. One disadvantage of different degree ranges is high computational costs. Moreover, fingerprints have clean direction field representations, which can be used to design suitable filter sets with low computational complexity. This article adopts modified Haar-like patterns to perform near-circle filter sets for acceptable feature coverage. In the processing of overlapping fingerprint automatic detection in a whole image, this article proposes an efficient method based on two statistical results, the mean and standard deviation in the frequency domain response under the discrete wavelet transform. To separate overlapping fingerprints, this article adopts the Gaussian matrix and discrete Fourier transform, where the correct angle is decided by automatic fingerprint detection. Through feature identification, this article proposes a multiple filter set for feature extraction and the efficient SVM-based classifier. In a performance comparison, using ten Haar-like patterns and a cascade classifier, which had a built-in open CV library as a benchmark, the proposed algorithm can reduce approximately 50% of computations on average and maintain an equal accuracy.