Person re-identification in surveillance camera videos is attracting widespread interest due to its increasing number of applications. It is being applied in the field of security, healthcare, product manufacturing, product sales and more. Though there are a variety of methods to do person reidentification, face verification-based methods are very much effective. In this study, a deep learning framework to perform face verification in videos is proposed. Face verification deep learning model development includes different stages like face recognition, cropping, alignment, augmentation, image enhancement and face image selection for model training. The authors have put forward innovative methods to be adopted in various stages of this sequence to improve the performance of the models. The focus of this study is on these image preprocessing stages of the process, rather than the deep learning part, which makes the approach unique. The overall model is improvised by increasing the efficiency of each of these stages by adopting methods like face recognition and cropping based on face landmarks, effective training image selection using face landmark symmetry, various image augmentation techniques including perspective transformation and image enhancement methods like contrast stretching and histogram equalization. An average two percent increase is obtained in the accuracy of the face verification models by applying these methods.

The transformer architecture has shown great capability in learning long-term dependency and works well in multiple domains. However, transformer has been less considered in audio-visual speech enhancement (AVSE) research, partly due to the convention that treats speech enhancement as a short-time signal processing task. In this paper, we challenge this common belief and show that an audio-visual transformer can significantly improve AVSE performance, by learning the long-term dependency of both intra-modality and inter-modality. We test this new transformer-based AVSE model on the GRID and AVSpeech datasets, and show that it beats several state-of-the-art models by a large margin.