Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (14): 96-104.DOI: 10.3778/j.issn.1002-8331.2311-0276
• Pattern Recognition and Artificial Intelligence • Previous Articles Next Articles
LI Ming, DANG Qingxia
Online:
2024-07-15
Published:
2024-07-15
李明,党青霞
LI Ming, DANG Qingxia. Lightweight Face Recognition Algorithm Combining Transformer and CNN[J]. Computer Engineering and Applications, 2024, 60(14): 96-104.
李明, 党青霞. 融合Transformer和CNN的轻量级人脸识别算法[J]. 计算机工程与应用, 2024, 60(14): 96-104.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2311-0276
[1] ADJABI I, OUAHABI A, BENZAOUI A, et al. Past, present, and future of face recognition: a review[J]. Electronics, 2020, 9(8): 1188. [2] SUN Y, LIANG D, WANG X G, et al. DeepID3: face recognition with very deep neural networks[J]. arXiv:1502.00873, 2015. [3] PARKHI O M, VEDALDI A, ZISSERMAN A. Deep face recognition[C]//Proceedings of the British Machine Vision Conference, 2015. [4] WEN Y D, ZHANG K P, LI Z F, et al. A discriminative feature learning approach for deep face recognition[C]//Proceedings of the European Conference on Computer Vision, 2016: 499-515. [5] DENG J K, GUO J, YANG J, et al. ArcFace: additive angular margin loss for deep face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(10): 5962-5979. [6] LEE J, WANG Y, CHO S. Angular margin-mining softmax loss for face recognition[J]. IEEE Access, 2022, 10: 43071-43080. [7] BOUTROS F, DAMER N, KIRCHBUCHNER F, et al. Elasticface: elastic margin loss for deep face recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 1578-1587. [8] ZHANG Y, HERDADE S, THADANI K, et al. Unifying margin-based softmax losses in face recognition[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023: 3548-3557. [9] SANG M, CHEN J X, LI M Z, et al. InterFace: adjustable angular margin inter-class loss for deep face recognition[J]. arXiv:2210.02018, 2022. [10] JIAO J C, LIU W L, MO Y K, et al. Dyn-arcFace: dynamic additive angular margin loss for deep face recognition[J]. Multimedia Tools and Applications, 2021, 80(17): 25741-25756. [11] DALVI J, BAFNA S, BAGARIA D, et al. A survey on face recognition systems[J]. arXiv:2201.02991, 2022. [12] BOUTROS F, DAMER N, FANG M L, et al. MixFaceNets: extremely efficient face recognition networks[C]//Proceedings of the IEEE International Joint Conference on Biometrics, 2021: 1-8. [13] CHEN S, LIU Y, GAO X, et al. MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices[C]//Proceedings of the Chinese Conference on Biometric Recognition, 2018: 428-438. [14] MARTINEZ-DIAZ Y, MENDEZ-VAZQUEZ H, NICOLAS-DIAZ M, et al. ShuffleFaceNet: a lightweight face architecture for efficient and highly-accurate face recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 2721-2728. [15] MARTINEZ-DIAZ Y, NICOLAS-DIAZ M, MENDEZ-VAZQUEZ H, et al. Benchmarking lightweight face architectures on specific face recognition scenarios[J]. Artificial Intelligence Review, 2021, 54: 6201-6244. [16] YAN M J, ZHAO M G, XU Z N, et al. VarGFaceNet: an efficient variable group convolutional neural network for lightweight face recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2020: 2647-2654. [17] TAN M X, LE Q V. MixConv: mixed depthwise convolutional kernels[C]//Proceedings of the British Machine Vision Conference, 2019. [18] HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017. [19] MA N N, ZHANG X Y, ZHENG H T, et al. ShuffleNet v2: practical guidelines for efficient CNN architecture design[C]//Proceedings of the European Conference on Computer Vision, 2018: 122-138. [20] CAI H, ZHU L G, HAN S. ProxylessNAS: direct neural architecture search on target task and hardware[C]//Proceedings of the International Conference on Learning Representations, 2019. [21] ZHANG Q, LI J J, YAO M, et al. VarGNet: variable group convolutional neural network for efficient embedded computing[J]. arXiv:1907.05653, 2019. [22] ZHANG P, ZHAO F, LIU P, et al. Efficient lightweight attention network for face recognition[J]. IEEE Access, 2022, 10: 31740-31750. [23] ALANSARI M, HAY O A, JAVED S, et al. GhostFaceNets: lightweight face recognition model from cheap operations[J]. IEEE Access, 2023, 11: 35429-35446. [24] DAI Y, SUN K, HUANG W, et al. Attention-based hierarchical pyramid feature fusion structure for efficient face recognition[J]. IET Image Processing, 2023, 17(8): 2399-2409. [25] LI H Y, HU J S, YU J W, et al. UFaceNet: research on multi-task face recognition algorithm based on CNN[J]. Algorithms, 2021, 14(9): 268. [26] BOUTROS F, SIEBKE P, KLEMT M, et al. PocketNet: extreme lightweight face recognition network using neural architecture search and multistep knowledge distillation[J]. IEEE Access, 2022, 10: 46823-46833. [27] WANG X B. Teacher guided neural architecture search for face recognition[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2021: 2817-2825. [28] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017, 30: 5998-6008. [29] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[C]//Proceedings of the International Conference on Learning Representations, 2021. [30] LIU Z, LIN Y T, CAO Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022. [31] MEHTA S, RASTEGARI M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer[J]. arXiv:2110.02178, 2021. [32] 杨鹤, 柏正尧. CoT-TransUNet: 轻量化的上下文Transformer医学图像分割网络[J]. 计算机工程与应用, 2023, 59(3): 218-225. YANG H, BAI Z Y. CoT-TransUNet: lightweight context Transformer medical image segmentation network[J]. Computer Engineering and Applications, 2023, 59(3): 218-225. [33] 项剑文, 陈泯融, 杨百冰. 结合Swin及多尺度特征融合的细粒度图像分类[J]. 计算机工程与应用, 2023, 59(20): 147-157. XIANG J W, CHEN M R, YANG B B. Fine-grained image classification combining swin and multi-scale feature fusion[J]. Computer Engineering and Applications, 2023, 59(20): 147-157. [34] 张朝阳, 张上, 王恒涛, 等. 多尺度下遥感小目标多头注意力检测[J]. 计算机工程与应用, 2023, 59(8): 227-238. ZHANG C Y, ZHANG S, WANG H T, et al. Multi-head attention detection of small targets in remote sensing at multiple scales[J]. Computer Engineering and Applications, 2023, 59(8): 227-238. [35] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision, 2018: 3-19. [36] ZHANG K P, ZHANG Z P, LI Z F, et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 2016, 23(10): 1499-1503. [37] HUANG G B, MATTAR M, BERG T, et al. Labeled faces in the wild: a database for studying face recognition in unconstrained environments[C]//Proceedings of the Workshop on Faces in “Real-Life” Images: Detection, Alignment, and Recognition, 2008. [38] ZHENG T Y, DENG W H, HU J N. Cross-age LFW: a database for studying cross-age face recognition in unconstrained environments[J]. arXiv:1708.08197, 2017. [39] ZHENG T Y, DENG W H. Cross-pose LFW: a database for studying cross-pose face recognition in unconstrained environments[R]. Beijing: Beijing University of Posts and Telecommunications, 2018. [40] SENGUPTA S, CHEN J C, CASTILLO C, et al. Frontal to profile face verification in the wild[C]//Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, 2016: 1-9. [41] MOSCHOGLOU S, PAPAIOANNOU A, SAGONAS C, et al. AgeDB: the first manually ected, in-the-wild age database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017: 51-59. [42] CAO Q, SHEN L, XIE W D, et al. VGGFace2: a dataset for recognising faces across pose and age[C]//Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition, 2018: 67-74. [43] WHITELAM C, TABORSKY E, BLANTON A, et al. IARPA Janus Benchmark?B face dataset[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017: 592-600. [44] MAZE B, ADAMS J, DUNCAN J A, et al. IARPA Janus Benchmark-C: face dataset and protocol[C]//Proceedings of the International Conference on Biometrics, 2018: 158-165. [45] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778. [46] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141. |
[1] | ZHANG Yangning, ZHU Jing, DONG Rui, YOU Zeshun, WANG Zhen. Discourse-Level Topic Segmentation Model with Multi-Level Information Enhanced Heterogeneous Graphs Network [J]. Computer Engineering and Applications, 2024, 60(9): 203-211. |
[2] | TAO Linjuan, HUA Gengxing, LI Bo. Aspect-Level Sentiment Analysis Based on Location-Enhanced Word Embeddings and GRU-CNN Model [J]. Computer Engineering and Applications, 2024, 60(9): 212-218. |
[3] | JIANG Jielin, ZHU Yongwei, XU Xiaolong, CUI Yan, ZHAO Yingnan. Chinese Short Text Classification with Hybrid Features and Multi-Head Attention [J]. Computer Engineering and Applications, 2024, 60(9): 237-243. |
[4] | LI Zhonghua, LIN Chujun, ZHU Hengliang, LIAO Shiyu, BAI Yunqi. Small Object Detection Based on Structure Perception and Global Context Information [J]. Computer Engineering and Applications, 2024, 60(9): 292-298. |
[5] | LIU Shipeng, NING Dejun, MA Jue. LSTformer Model for Photovoltaic Power Prediction [J]. Computer Engineering and Applications, 2024, 60(9): 317-325. |
[6] | LIAN Lu, TIAN Qichuan, TAN Run, ZHANG Xiaohang. Research Progress of Image Style Transfer Based on Neural Network [J]. Computer Engineering and Applications, 2024, 60(9): 30-47. |
[7] | SHI Tao, CUI Jie, LI Song. Algorithm for Real-Time Vehicle Detection from UAVs Based on Optimizing and Improving YOLOv8 [J]. Computer Engineering and Applications, 2024, 60(9): 79-89. |
[8] | DOU Zhi, GAO Haoran, LIU Guoqi, CHANG Baofang. Small Sample Steel Plate Defect Detection Algorithm of Lightweight YOLOv8 [J]. Computer Engineering and Applications, 2024, 60(9): 90-100. |
[9] | WANG Ru, LIU Daming, ZHANG Jian. Wear-YOLO:Research on Detection Methods of Safety Equipment for Power Personnel in Substations [J]. Computer Engineering and Applications, 2024, 60(9): 111-121. |
[10] | CAI Teng, CHEN Cifa, DONG Fangmin. Low-Light Object Detection Combining Transformer and Dynamic Feature Fusion [J]. Computer Engineering and Applications, 2024, 60(9): 135-141. |
[11] | YANG Wentao, LEI Yuqi, LI Xingyue, ZHENG Tiancheng. Chinese Long Text Classification Model Based on BERT Fused Chinese Input Methods and BLCG [J]. Computer Engineering and Applications, 2024, 60(9): 196-202. |
[12] | DENG Xiquan, CHEN Gang. ConvUCaps: Medical Image Segmentation Model Based on Convolutional Capsule Network [J]. Computer Engineering and Applications, 2024, 60(8): 258-266. |
[13] | WANG Yonggui, WANG Xinru. Multi-View Group Recommendation Integrating Self-Attention and Graph Convolution [J]. Computer Engineering and Applications, 2024, 60(8): 287-295. |
[14] | SUN Shilei, LI Ming, LIU Jing, MA Jingang, CHEN Tianzhen. Research Progress on Deep Learning in Field of Diabetic Retinopathy Classification [J]. Computer Engineering and Applications, 2024, 60(8): 16-30. |
[15] | XIE Weiyu, ZHANG Qiang. Review on Detection of Drones and Birds in Photoelectric Images Based on Deep Learning Convolutional Neural Network [J]. Computer Engineering and Applications, 2024, 60(8): 46-55. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||