It is a challenging task to interpret sign language automatically, as it comprises high-level vision features to accurately understand and interpret the meaning of the signer or vice versa. In the current study, we automatically distinguish hand signs and classify seven basic gestures representing symbolic emotions or expressions like happy, sad, neutral, disgust, scared, anger, and surprise. Convolutional Neural Network is a famous method for classifications using vision-based deep learning; here in the current study, proposed transfer learning using a well-known architecture of VGG16 to speed up the convergence and improve accuracy by using pre-trained weights. We obtained a high accuracy of 99.98% of the proposed architecture with a minimal and low-quality data set of 455 images collected by 65 individuals for seven hand gesture classes. Further, compared the performance of VGG16 architecture with two different optimizers, SGD, and Adam, along with some more architectures of AlexNet, LeNet05, and ResNet50.