LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace
For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/1/24 |
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| author | Zhengwei Ren Xinyu Liu Jing Xu Yongsheng Zhang Ming Fang |
| author_facet | Zhengwei Ren Xinyu Liu Jing Xu Yongsheng Zhang Ming Fang |
| author_sort | Zhengwei Ren |
| collection | DOAJ |
| description | For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface’s weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications. |
| format | Article |
| id | doaj-art-10bfa7d58eb44544b363b76cda7898a1 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-10bfa7d58eb44544b363b76cda7898a12025-01-24T13:36:19ZengMDPI AGJournal of Imaging2313-433X2025-01-011112410.3390/jimaging11010024LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFaceZhengwei Ren0Xinyu Liu1Jing Xu2Yongsheng Zhang3Ming Fang4School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, ChinaSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, ChinaSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, ChinaSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, ChinaSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, ChinaFor surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface’s weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications.https://www.mdpi.com/2313-433X/11/1/24face recognitionRetinaFaceAdafaceobject trackingdeep learninglow resolution |
| spellingShingle | Zhengwei Ren Xinyu Liu Jing Xu Yongsheng Zhang Ming Fang LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace Journal of Imaging face recognition RetinaFace Adaface object tracking deep learning low resolution |
| title | LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace |
| title_full | LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace |
| title_fullStr | LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace |
| title_full_unstemmed | LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace |
| title_short | LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace |
| title_sort | littlefacenet a small sized face recognition method based on retinaface and adaface |
| topic | face recognition RetinaFace Adaface object tracking deep learning low resolution |
| url | https://www.mdpi.com/2313-433X/11/1/24 |
| work_keys_str_mv | AT zhengweiren littlefacenetasmallsizedfacerecognitionmethodbasedonretinafaceandadaface AT xinyuliu littlefacenetasmallsizedfacerecognitionmethodbasedonretinafaceandadaface AT jingxu littlefacenetasmallsizedfacerecognitionmethodbasedonretinafaceandadaface AT yongshengzhang littlefacenetasmallsizedfacerecognitionmethodbasedonretinafaceandadaface AT mingfang littlefacenetasmallsizedfacerecognitionmethodbasedonretinafaceandadaface |