Many Facial expression recognition methods have achieved great success, but they only considered front facial images or facial images close to the front. Besides, unlike in-the-laboratory datasets, the facial images in the real world (or in the wild) are without lighting and pose control, so that it is a big challenge to recognize these facial expressions. In this paper, the PIDViT (i.e., Pose-Invariant Distilled Vision Transformer) using the teacher-student model for the probability distributions of facial expressions of frontal and multi-pose faces was proposed and solved the pose variance and occlusion issues on expression recognition. First, the multi-pose face dataset FairFace-3D was built from the original FairFace and then used to train pose-invariance on the PIDViT. The PIDViT was trained in two stages; stage 1 is to train the PIDViT to achieve the consistency of facial expressions between frontal faces and multi-pose faces, and stage 2 is to use the student model pre-trained in stage 1 and train facial expressions further on target datasets. Finally, comprehensive experiments were conducted on three in the wild facial expression datasets, and the results validates the generalization of the PIDViT and its superiority over most state-of-the-art models.

Natural Language Processing enables computers to understand human natural languages and assists humans to perform many tasks. Recently, language research was gradually moving towards the field of natural language generation, and lyrics generation is one of these research directions. In the past, many studies have explored how to generate lyrics using seeds or prompt words. But, they usually ignored the relationship between music and lyrics and even melody emotions when generating lyrics. In this paper, we propose the Chinese lyrics generation system based on notes and melody emotions. In the first phase, a support vector regression model is trained to detect melody emotions. In the second phase, a sequence-to-sequence model is trained to generate lyrics using notes, melody emotions, and lyrics. Finally, we conduct the experiments and evaluate the detected melody emotions and the generated lyrics. Results show that the generated lyrics have fluent sentences, and the sentences are with connected semantics.

For image matching, the scale invariant feature transform (SIFT) algorithm is a commonly used one. They are invariant to image rotation, scale zooming, and partially invariant to change in illumination and 3D camera viewpoint. Affine SIFT (ASIFT) is an extension of SIFT, which solves the problem when images are captured at different angles. However, ASIFT has higher computational complexity than SIFT, due to a huge amount of features in the images. Therefore, in this study, a Hadoop‐based image retrieval system is proposed to solve the ASIFT shortcomings of high computation by the MapReduce technology. The system uses a combination of the Bag‐of‐Words method and support vector machine. Finally, the experimental results verify that the proposed method is more effective than the other state‐of‐the‐art methods for a variety of datasets.