The automaticly individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. In this paper, we applied the object detection method and deep convolutional neural network models to individual identification with the images of 38 Amur tigers in the Northeast Tiger Forest Park (West 126°36', North 45°49') and Guaipo Tiger Park (West 123°37', North 42°4'). Firstly, the Canon EOS 200D camera was used to establish the data set containing 13579 pictures of Amur tiger from different angles. Since the tiger's body stripes of two sides are not symmetrical, single shot multibox detector (SSD) was used to automatically intercept and distinguish the left and right body stripes as well as the face of tiger, which greatly saved the time of manual interception. On the basis of the interception image results, the data were enhanced to 5 times by the up, down, left and right transformation. Then, LeNet, AlexNet, ZFNet, VGG16 and ResNet34 were used for individual identification. Furthermore, the pooling model was optimized by using different combinations of average pooling and maximum pooling, and the dropout operations with probabilities of 0.1, 0.2, 0.3 and 0.4 were introduced to prevent overfitting. The experiment shows that the target detection model in this study takes less time than manual interception. It can intercept and segment the stripes in different parts of the tiger with 0.6 seconds for one image, which is much faster than manual interception, and the accuracy rate on the test set can reach up to 97.4%. The target parts can be correctly identified and segmented for Amur tiger images with different kinds of positions. Finally, based on the experiment results, we found that the accuracy of ResNet34 is better than that of other network models. The recognition accuracies of left, right stripe and face images are 93.75%, 97.01%, and 86.28%, respectively. The recognition accuracy of right stripes is better than left stripes and face parts. This study can provide technical support for automatic camera image recognition of wild tigers. The method in this paper can be applied to the identification of individual in the species which have distinct streaks or spots on the body. In the future work, the image dadaset of individual Amur tigers will be expanded and much more image data will be selected for training set so as to make the network more adaptable and realize more accurate individual identification.