In this study, we investigate the problem of detecting humans fall from video images. Many of the existing methods try to solve the problem by manually defining a set of hand-crafted features for detecting fall, which is not only a suboptimal approach but also cumbersome. On the contrary, the proposed method puts the burden of feature extraction on a pre-trained deep neural network. In this way, we can extract a comprehensive set of conceptual features automatically and efficiently. An important challenge of employing deep neural networks is the need for a large collection of training data. While the available labeled data for human fall detection is very limited, we propose three approaches based on transfer learning, and we trained them on two standard RGB and depth datasets for fall detection. The pre-trained models explored in this study are VGG16, Inception V3, and ResNet50. Support vector machine and logistic regression are used to classify the extracted features from videos into two classes of fall and normal daily activities. The experimental results obtained from the proposed approach suggest that the transfer learning tactic is able to compensate for the low training data issue. It is also shown that the proposed approach can efficiently extract important features from the sequences of video and boost the accuracy of the system on the task of human fall detection.