A novel algorithm for incomplete fingerprint recognition is proposed in this paper using fusion features and pattern entropy based similarity measure. Because of incomplete fingerprint's unique characteristic of information loss, the recognition performance is mainly restricted by two critical problems: extracting features containing sufficient information and measuring similarity more effectively. For the first problem, minutiae and orientation field features are fused to get more comprehensive information and to improve the scale and rotation invariability. For the second, the pattern entropy is introduced to measure the coherency of correspondences between two feature sets to eliminate false match. The extensive experiments are done and compared with existing method on fingerprint databases and made thorough comparisons. Experimental results show that the proposed scheme has more efficient ability on separating genuine and impostor pairs and performs well in both accuracy and speed.%该文提出了一种新的残缺指纹识别算法,在应用融合特征的同时,利用模式熵进行相似性度量.由于残缺指纹的特有性质,识别结果主要由两方面决定,即提取包含足够信息的特征以及有效的相似性度量方式.对于第1个问题,该文将细节点和方向场特征进行有效融合,来得到更全面的信息,并提高尺度和旋转不变性.对于第2个问题,通过引入模式熵度量两个特征点集之间的一致性,并以此来消除误匹配.在指纹库中进行的大量实验以及同其他方法的充分比较表明,该文提出的算法在准确率和速度上都取得了较优的性能.
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