3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss
Abstract Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face resto...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2021-01-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12005 |
| _version_ | 1797430537147645952 |
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| author | Sahil Sharma Vijay Kumar |
| author_facet | Sahil Sharma Vijay Kumar |
| author_sort | Sahil Sharma |
| collection | DOAJ |
| description | Abstract Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face restoration and recognition. A novel three‐dimensional (3D) landmark‐based face restoration method is proposed. 3D facial landmarks are used in the face recognition technique. It uses the principle of reflection and mid‐face plane for the restoration of facial landmarks. By using the restored 3D face, a deep learning‐based face recognition system is developed. It utilizes the concept of deep features from variational autoencoders. Further, these deep feature embeddings are trained using triplet loss training to increase the distance between embeddings of different persons and decreasing the distance between embeddings of the same person. These trained embeddings are used in support vector machine for prediction. The proposed framework is compared with recently developed face recognition techniques in terms of computational time. The proposed technique is able to recognize the person's face with better accuracy than the existing methods. Further, ablation studies are conducted to test the robustness of the proposed technique. |
| first_indexed | 2024-03-09T09:30:03Z |
| format | Article |
| id | doaj.art-70bc50bff9804803a7d794f95d0c3e6d |
| institution | Directory Open Access Journal |
| issn | 2047-4938 2047-4946 |
| language | English |
| last_indexed | 2024-03-09T09:30:03Z |
| publishDate | 2021-01-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj.art-70bc50bff9804803a7d794f95d0c3e6d2023-12-02T04:32:43ZengHindawi-IETIET Biometrics2047-49382047-49462021-01-01101879810.1049/bme2.120053D landmark‐based face restoration for recognition using variational autoencoder and triplet lossSahil Sharma0Vijay Kumar1Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala IndiaComputer Science and Engineering Department National Institute of Technology Hamirpur IndiaAbstract Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face restoration and recognition. A novel three‐dimensional (3D) landmark‐based face restoration method is proposed. 3D facial landmarks are used in the face recognition technique. It uses the principle of reflection and mid‐face plane for the restoration of facial landmarks. By using the restored 3D face, a deep learning‐based face recognition system is developed. It utilizes the concept of deep features from variational autoencoders. Further, these deep feature embeddings are trained using triplet loss training to increase the distance between embeddings of different persons and decreasing the distance between embeddings of the same person. These trained embeddings are used in support vector machine for prediction. The proposed framework is compared with recently developed face recognition techniques in terms of computational time. The proposed technique is able to recognize the person's face with better accuracy than the existing methods. Further, ablation studies are conducted to test the robustness of the proposed technique.https://doi.org/10.1049/bme2.12005computer visionface recognitionimage representationlearning (artificial intelligence) |
| spellingShingle | Sahil Sharma Vijay Kumar 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss IET Biometrics computer vision face recognition image representation learning (artificial intelligence) |
| title | 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss |
| title_full | 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss |
| title_fullStr | 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss |
| title_full_unstemmed | 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss |
| title_short | 3D landmark‐based face restoration for recognition using variational autoencoder and triplet loss |
| title_sort | 3d landmark based face restoration for recognition using variational autoencoder and triplet loss |
| topic | computer vision face recognition image representation learning (artificial intelligence) |
| url | https://doi.org/10.1049/bme2.12005 |
| work_keys_str_mv | AT sahilsharma 3dlandmarkbasedfacerestorationforrecognitionusingvariationalautoencoderandtripletloss AT vijaykumar 3dlandmarkbasedfacerestorationforrecognitionusingvariationalautoencoderandtripletloss |