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A core challenge faced by the majority of individuals with Autism Spectrum Disorder (ASD) is an impaired ability to infer other people's emotions based on their facial expressions. With significant recent advances in machine learning, one potential approach to leveraging technology to assist such individuals to better recognize facial expressions and reduce the risk of possible loneliness and depression due to social isolation is the design of computer vision-driven facial expression recognition systems. Motivated by this social need as well as the low latency requirement of such systems, this study explores a novel deep time windowed convolutional neural network design (TimeConvNets) for the purpose of real-time video facial expression recognition. More specifically, we explore an efficient convolutional deep neural network design for spatiotemporal encoding of time windowed video frame sub-sequences and study the respective balance between speed and accuracy. Furthermore, to evaluate the proposed TimeConvNet design, we introduce a more difficult dataset called BigFaceX, composed of a modified aggregation of the extended Cohn-Kanade (CK+), BAUM-1, and the eNTERFACE public datasets. Different variants of the proposed TimeConvNet design with different backbone network architectures were evaluated using BigFaceX alongside other network designs for capturing spatiotemporal information, and experimental results demonstrate that TimeConvNets can better capture the transient nuances of facial expressions and boost classification accuracy while maintaining a low inference time.