# DCM2Net: an improved face recognition model for panoramic stereoscopic videos

## TL;DR

This paper introduces DCM2Net, a new face recognition model designed to handle the challenges of identifying faces in panoramic stereo videos.

## Contribution

The novelty lies in integrating and redistributing channel feature information to improve face recognition in panoramic stereo videos.

## Key findings

- DCM2Net outperforms existing models on popular and panoramic datasets.
- The model effectively handles face deformation in panoramic stereo videos.
- A live system was built to demonstrate real-time face recognition results.

## Abstract

The panoramic stereo video has brought a new visual experience for the audience with its immersion and stereo effect. In panoramic stereo video, the face is an important element. However, the face image in panoramic stereo video has varying degrees of deformation. This brings new challenges to face recognition. Therefore, this paper proposes a face recognition model DCM2Net (Deformable Convolution MobileFaceNet) for panoramic stereo video. The model mainly integrates the feature information between channels during feature fusion, redistributes the information between channels in the deeper part of the network, and fully uses the information between different channels for feature extraction. This paper also built a panoramic stereo video live system, using the DCM2Net model to recognize the face in panoramic stereo video, and the recognition results are displayed in the video. After experiments on different datasets, the results show that our model has better results on popular datasets and panoramic datasets.