|
|
|
| Target recognition based on gated feature fusion and center loss |
Jian-wen MO( ),Jin LI,Xiao-dong CAI*(),Jin-wei CHEN |
| School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China |
|
|
|
Abstract A target identification method based on gated feature fusion with center loss was proposed, aiming at the problems of target activity, light and camera distance. Gated feature fusion was designed to compensate for the decrease in identity recognition accuracy when the single feature information was lost. Gated structure guidance network evaluated the contribution of input facial and pedestrian features, and weights were assigned according to the contribution to produce a more recognizable identity feature. By adding a center loss function, the intra-class distance of the features was reduced under the guidance network, making the features more discriminative. The final recognition accuracy of the proposed method on the self-constructed dataset could reach up to 76.35%, which was better than that of single-feature recognition methods and multiple fusion methods. The average recognition accuracy could be improved by 2.63% with the proposed fusion loss function.
|
|
Received: 22 September 2022
Published: 18 October 2023
|
|
|
| Fund: 国家自然科学基金资助项目（62001133, 62177012）; 广西创新驱动发展专项项目（AA20302001）；广西无线宽带通信与信号处理重点实验室基金资助项目（GXKL06200114） |
|
Corresponding Authors:
Xiao-dong CAI
E-mail: Mo_jianwen@126.com;caixiaodong@guet.edu.cn
|
基于门控特征融合与中心损失的目标识别
针对目标活动、光线及摄像头距离等问题，提出一种基于门控特征融合与中心损失的目标识别方法. 门控特征融合是为了弥补单一特征信息丢失时，身份识别准确率下降的缺陷. 门控结构指导网络对输入的人脸、行人特征进行贡献量评估, 再根据贡献量去分配权值，组合产生识别性更强的身份特征. 通过添加中心损失函数，在引导网络下减少了特征的类内距离，使得特征更具判别性. 实验结果表明，在自建数据集上所提方法的最终识别准确率最高可以达到76.35%，优于单特征识别方法以及多种融合方法，使用所提的融合损失函数后，平均识别准确率可提高2.63%.
关键词：
身份识别,
监控场景,
特征融合,
门控机制,
中心距离损失
|
|
| [1] |
WANG K, WANG S, ZHANG P, et al. An efficient training approach for very large scale face recognition [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 4083-4092.
|
|
|
| [2] |
ZHU H, KE W, LI D, et al. Dual cross-attention learning for fine-grained visual categorization and object re-identification [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 4692-4702.
|
|
|
| [3] |
YE F, YANG J A deep neural network model for speaker identification[J]. Applied Sciences, 2021, 11 (8): 3603
doi: 10.3390/app11083603
|
|
|
| [4] |
YE M, SHEN J, SHAO L Visible-infrared person re-identification via homogeneous augmented tri-modal learning[J]. IEEE Transactions on Information Forensics and Security, 2020, 16: 728- 739
|
|
|
| [5] |
QIAN Y, CHEN Z, WANG S Audio visual deep neural network for robust person verification[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 1079- 1092
doi: 10.1109/TASLP.2021.3057230
|
|
|
| [6] |
SARANGI P P, NAYAK D R, PANDA M, et al A feature-level fusion based improved multimodal biometric recognition system using ear and profile face[J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13: 1867- 1898
|
|
|
| [7] |
GUO W, WANG J, WANG S Deep multimodal representation learning: a survey[J]. IEEE Access, 2019, 7: 63373- 63394
doi: 10.1109/ACCESS.2019.2916887
|
|
|
| [8] |
AREVALO J, SOLORIO T, MONTESYGOMEZ M, et al Gated multimodal networks[J]. Neural Computing and Applications, 2020, 32: 10209- 10228
doi: 10.1007/s00521-019-04559-1
|
|
|
| [9] |
DICKSON M C, BOSMAN A S, MALAN K M. Hybridised loss functions for improved neural network generalisation [C]// Pan African Artificial Intelligence and Smart Systems: First International Conference. Cham: SIP, 2022: 169-181.
|
|
|
| [10] |
WEN Y, ZHANG K, LI Z, et al. A discriminative feature learning approach for deep face recognition [C]// Computer Vision ECCV 2016: 14th European Conference. Netherlands: SIP, 2016: 499-515.
|
|
|
| [11] |
DENG J, GUO J, XUE N, et al. Arcface: Additive angular margin loss for deep face recognition [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4690-4699.
|
|
|
| [12] |
SUN Y, ZHENG L, YANG Y, et al. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline) [C]// Proceedings of the European Conference on Computer Vision. Munich: [s. n. ], 2018: 480-496.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
