Detecting spoofing compared to a live trait is a critical problem in the biometric authentication. In this paper, we present a novel method to detect fake fingerprint attacks based on the ensemble of image quality assessments (IQAs). The key idea of the proposed method is to combine
quality scores obtained from multiple local regions, which are input into the linear SVM classifier to determine whether the given fingerprint is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies fake fingerprints
even with small partial distortions. Moreover, the proposed method does not require any additional device. Experimental results on the mobile device show that the proposed method is effective for fingerprint liveness detection in real-world scenarios.