Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning
Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial ima...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/3/59 |
| _version_ | 1797240480549830656 |
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| author | Yanhua Huang Zhendong Wu Juan Chen Hui Xiang |
| author_facet | Yanhua Huang Zhendong Wu Juan Chen Hui Xiang |
| author_sort | Yanhua Huang |
| collection | DOAJ |
| description | Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect. |
| first_indexed | 2024-04-24T18:08:06Z |
| format | Article |
| id | doaj.art-702e1639040a4daaaab3053bb9bcd0a3 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-04-24T18:08:06Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-702e1639040a4daaaab3053bb9bcd0a32024-03-27T13:48:51ZengMDPI AGJournal of Imaging2313-433X2024-02-011035910.3390/jimaging10030059Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature LearningYanhua Huang0Zhendong Wu1Juan Chen2Hui Xiang3School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaPersonal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect.https://www.mdpi.com/2313-433X/10/3/59facial recognitionprivacy protectionlocal randomization and learningvisual information eliminationprivacy-preserving face recognition |
| spellingShingle | Yanhua Huang Zhendong Wu Juan Chen Hui Xiang Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning Journal of Imaging facial recognition privacy protection local randomization and learning visual information elimination privacy-preserving face recognition |
| title | Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning |
| title_full | Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning |
| title_fullStr | Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning |
| title_full_unstemmed | Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning |
| title_short | Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning |
| title_sort | privacy preserving face recognition method based on randomization and local feature learning |
| topic | facial recognition privacy protection local randomization and learning visual information elimination privacy-preserving face recognition |
| url | https://www.mdpi.com/2313-433X/10/3/59 |
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