Text-to-image generation is a method used for generating images related to given textual descriptions. It has a significant influence on many research areas as well as a diverse set of applications (e.g., photo-searching, photo-editing, art generation, computer-aided design, image reconstruction, captioning, and portrait drawing). The most challenging task is to consistently produce realistic images according to given conditions. Existing algorithms for text-to-image generation create pictures that do not properly match the text. We considered this issue in our study and built a deep learning-based architecture for semantically consistent image generation: recurrent convolutional generative adversarial network (RC-GAN). RC-GAN successfully bridges the advancements in text and picture modelling, converting visual notions from words to pixels. The proposed model was trained on the Oxford-102 flowers dataset, and its performance was evaluated using an inception score and PSNR. The experimental results demonstrate that our model is capable of generating more realistic photos of flowers from given captions, with an inception score of 4.15 and a PSNR value of 30.12 dB, respectively. In the future, we aim to train the proposed model on multiple datasets.

A picture is worth a thousand words to convey information during daily life communication. Recognizing a person’s emotions from facial expressions (FEs) has become a prevalent research field in the past few decades. Deep learning (DL) models, mainly deep convolutional neural networks (DCNNs), are trending in the last few years to perform recognition/classification tasks. Several prior DCNNs have resulted in good recognition accuracy for facial emotion recognition (FER) systems. Still, there is a need for an effective as well as efficient FER system that can recognize the FEs irrespective of the illumination conditions, subjects’ gender, age range, geographical locations, race, etc. In this work, we have presented a novel lightweight DCNN model that can recognize the FEs in the aforementioned conditions. We have made it lightweight by optimal selection of its hidden layers, which ultimately resulted in the reduced number of floating point operations (FLOPs). We have embedded the data augmentation step into the model’s training phase to enhance its generalization ability. An early stopping criterion is also introduced to prevent the model’s overfitting. We have trained and evaluated the performance of the proposed model on widely used benchmarks for FEs databases. We have selected five diverse databases (two collected in the lab, one based on stylized cartoon characters, and two collections in an unconstrained realistic environment), including CK+, Karolinska Directed Emotional Faces (KDEF), Facial Expression Research Group (FERG), Facial Expression Recognition‐2013 (FER‐2013), and Real‐World Affective Faces Database (RaF‐DB). The recognition accuracy of 99.98%, 99.25%, 88.17%, 84.09%, 69.87%, and 69.16% is achieved for the FERG, CK+, KDEF, RaF‐DB, FER‐2013 human faces (FER‐2013_H), and FER‐2013 complete (FER‐2013_C) database, respectively. Our model outperforms the number of state‐of‐the‐art FER approaches in terms of recognition accuracy and FLOPs.