Human Body Model based ID using Shape and Pose Parameters (2312.03227v1)
Abstract: We present a Human Body model based IDentification system (HMID) system that is jointly trained for shape, pose and biometric identification. HMID is based on the Human Mesh Recovery (HMR) network and we propose additional losses to improve and stabilize shape estimation and biometric identification while maintaining the pose and shape output. We show that when our HMID network is trained using additional shape and pose losses, it shows a significant improvement in biometric identification performance when compared to an identical model that does not use such losses. The HMID model uses raw images instead of silhouettes and is able to perform robust recognition on images collected at range and altitude as many anthropometric properties are reasonably invariant to clothing, view and range. We show results on the USF dataset as well as the BRIAR dataset which includes probes with both clothing and view changes. Our approach (using body model losses) shows a significant improvement in Rank20 accuracy and True Accuracy Rate on the BRIAR evaluation dataset.
- Expanding accurate person recognition to new altitudes and ranges: The briar dataset. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 593–602, 2023.
- Arcface: Additive angular margin loss for deep face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):5962–5979, oct 2022.
- J. Han and B. Bhanu. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2):316–322, 2005.
- End-to-end recovery of human shape and pose. In IEEE Conference on Computer Vision and Pattern Recognition, 2018.
- Learning 3D human dynamics from video. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5614–5623, 2019.
- Neural 3D mesh renderer. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3907–3916, 2018.
- Vibe: Video inference for human body pose and shape estimation. In IEEE Conference on Computer Vision and Pattern Recognition, 2020.
- Self-correction for human parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
- End-to-end model-based gait recognition using synchronized multi-view pose constraint. In IEEE/CVF International Conference on Computer Vision Workshops, 2021.
- End-to-end model-based gait recognition. In Asian Conference on Computer Vision, 2020.
- Pose-based temporal-spatial network (ptsn) for gait recognition with carrying and clothing variations. In Biometric Recognition: 12th Chinese Conference, CCBR 2017, Shenzhen, China, October 28-29, 2017, Proceedings 12, pages 474–483. Springer, 2017.
- A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition, 98:107069, 2020.
- Smpl: A skinned multi-person linear model. ACM transactions on graphics (TOG), 34(6):1–16, 2015.
- Recognizing people by body shape using deep networks of images and words. arXiv preprint arXiv:2305.19160, 2023.
- A brief survey on person recognition at a distance. arXiv preprint arXiv:2212.08969, 2022.
- Star: Sparse trained articulated human body regressor. In European Conference on Computer Vision, 2020.
- Accelerating 3D deep learning with pytorch3d. arXiv preprint arXiv:2007.08501, 2020.
- The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):162–177, 2005.
- Geinet: View-invariant gait recognition using a convolutional neural network. In 2016 International Conference on Biometrics (ICB), pages 1–8. IEEE, 2016.
- On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Transactions on Circuits and Systems for Video Technology, 29(9):2708–2719, 2017.
- On automated model-based extraction and analysis of gait. In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., pages 11–16. IEEE, 2004.
- Human identification using temporal information preserving gait template. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2164–2176, 2011.
- A comprehensive study on cross-view gait based human identification with deep cnns. IEEE transactions on pattern analysis and machine intelligence, 39(2):209–226, 2016.
- Automated person recognition by walking and running via model-based approaches. Pattern recognition, 37(5):1057–1072, 2004.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.