A difficult problem in the study of criminology is the determination of age and other personal characteristics of criminal suspects, especially the use of fingerprints. For a long time, many scholars noted subtle changes in fingerprints with an increase in age, but presently there is a lack of specific correlation between the characteristics of fingerprints that change with age and the age of the individual. In this work, we use the Leica M125 volumetric microscope to measure right thumb wrinkles, density of friction ridges, width of friction ridges and small furrows, fine point lines and flexion crease in 1510 samples. The correlation between feature information in the right thumb and age was analyzed with SPSS software. Variables showing high correlation were selected for multiple linear regression. We found features of wrinkles, friction ridges density, width of friction ridges and small furrows, fine point lines and flexor crease in fingerprints all correlated with age, but the goodness of fit of the multiple regression model based on these variables was not high. Then we used principal component analysis to recombine the variables and conducted multiple regression analysis again. An ideal model was still not obtained, and we believe this model cannot be applied to the practice for accurate fingerprint age analysis. For research on fingerprints and age, these anatomical features listed above can be used as for reference. Age information that is difficult to measure and express (characteristics such as edge shape of friction ridges, degree of ambiguity of fingerprint marks, and perspiration holes) may play a key role in fingerprints age analysis, therefore such researches need to be continued.