In view of the problem of low recognition accuracy caused by 3D palmprint recognition due to noise interference and neglect of adjacent depth information,a 3D palmprint recognition method combining potential texture and surface consistency is proposed.First,the energy local edge binary code (ELEBC) is used to extract the potential texture direction information from the energy map to eliminate the noise.Then,surface consistency is obtained by mean block pattern surface type (MBST).Finally,the principal component analysis (PCA) is used to reduce the data dimension,and the final recognition result is obtained after decision-level fusion.Relevant experiments are carried out using the 3D palmprint database of Hong Kong Polytechnic University,and the results show that it has significant advantages over other novel algorithms.The maximum correct recognition rate can reach 99.71%,and the recognition classification time is kept below 0.5 s.Therefore,the proposed scheme not only has accurate recognition effect,but also can meet the real-time requirements,and its application value is obvious.