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The performance of automatic fingerprint identification system relies heavily on the quality of the fingerprint images. Poor quality images result in missing or spurious features, thus degrading the performance of the identification system. Therefore, it is important for a fingerprint identification system to estimate the quality of the captured fingerprint images. In this paper, a new method based on Principal Component Analysis (PCA) is proposed for fingerprint quality measure. PCA is a common and useful statistical technique for finding patterns in data of high dimension. It can be found that fingerprint patches in a local neighborhood form a simple and regular circular manifold topology in a high-dimensional space. The characterization of manifold topology represents the local properties of the fingerprint. In our method, we first extract two novel features from the expected manifold topology. Then a local block measure of quality is generated according to these two features using multiplication rules. Finally, incorporating the normalized Harris-corner strength (HCS) as weighted value into local block quality measure, we obtain a global quality of a fingerprint image. The proposed method has been evaluated on the databases of fingerprint verification competition 2004DB1 (FVC2004) and our private database(AES2501). The experimental results confirm that the proposed algorithm is simple and effective for fingerprint image quality measure.