Aiming at the problem that the information missing gestures recognition based on deep learning needs a large amount of labeled. The deeper the network needs more parameters, we first collect a data set called IMG_NUIST which consists of information missing gestures and full gestures. Then we propose a new gesture recognition model CLGR, the inter-class and intra-class similarities constraints enhance the feature learning performance of the model. Extensive experiments are conducted on two classic datasets (ASL Alphabet and NUS I) and the proposed IMG_NUIST dataset. The experimrnt results are shown as follows: 1) in the ablation study, contrastive learning can effectively improve recognition accuracy up to over 98.60% and the model convergence speed are significantly accelerated. 2) In the comparative experiments with two recent works and two contrastive learning models, the computational complexity of CLGR is 41.4% simpler than that of the two comparison models on average. CLCR can recognize the gestures with missing information and works well for those gestures with cluttered backgrounds. The gesture recognition accuracy of CLCR on the NUS I and IMG_NUSIT data sets outperforms the four comparison methods and is only 0.43% lower than the best result on ASL. Especially on the NUS I dataset, CLCR increases the recognition accuracy of gestures by 17.35% on average. The experimental results show that the proposed model is significantly effective for gesture recognition tasks with missing information and cluttered background with fast convergence speed and low computational complexity, and it is practical.