Profile to frontal face recognition in the wild using coupled conditional generative adversarial network
Abstract In recent years, with the advent of deep‐learning, face recognition (FR) has achieved exceptional success. However, many of these deep FR models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is tha...
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | IET Biometrics |
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| Online Access: | https://doi.org/10.1049/bme2.12069 |
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| author | Fariborz Taherkhani Veeru Talreja Jeremy Dawson Matthew C. Valenti Nasser M. Nasrabadi |
| author_facet | Fariborz Taherkhani Veeru Talreja Jeremy Dawson Matthew C. Valenti Nasser M. Nasrabadi |
| author_sort | Fariborz Taherkhani |
| collection | DOAJ |
| description | Abstract In recent years, with the advent of deep‐learning, face recognition (FR) has achieved exceptional success. However, many of these deep FR models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose‐invariant deep representations that are useful for profile FR. In this paper, the authors hypothesise that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. The authors look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. A coupled conditional generative adversarial network (cpGAN) structure is leveraged to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN‐based sub‐networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub‐network tends to find a projection that maximises the pair‐wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of the authors’ approach compared with the state of the art is demonstrated using the CFP, CMU Multi‐PIE, IARPA Janus Benchmark A, and IARPA Janus Benchmark C datasets. Additionally, the authors have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal FR. The authors have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, the authors have also evaluated the authors’ cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset. |
| format | Article |
| id | doaj-art-bc784d1fa9524904839aa2831eb01734 |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-bc784d1fa9524904839aa2831eb017342025-02-03T06:47:38ZengWileyIET Biometrics2047-49382047-49462022-05-0111326027610.1049/bme2.12069Profile to frontal face recognition in the wild using coupled conditional generative adversarial networkFariborz Taherkhani0Veeru Talreja1Jeremy Dawson2Matthew C. Valenti3Nasser M. Nasrabadi4Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown West Virginia USALane Department of Computer Science and Electrical Engineering West Virginia University Morgantown West Virginia USALane Department of Computer Science and Electrical Engineering West Virginia University Morgantown West Virginia USALane Department of Computer Science and Electrical Engineering West Virginia University Morgantown West Virginia USALane Department of Computer Science and Electrical Engineering West Virginia University Morgantown West Virginia USAAbstract In recent years, with the advent of deep‐learning, face recognition (FR) has achieved exceptional success. However, many of these deep FR models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose‐invariant deep representations that are useful for profile FR. In this paper, the authors hypothesise that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. The authors look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. A coupled conditional generative adversarial network (cpGAN) structure is leveraged to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN‐based sub‐networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub‐network tends to find a projection that maximises the pair‐wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of the authors’ approach compared with the state of the art is demonstrated using the CFP, CMU Multi‐PIE, IARPA Janus Benchmark A, and IARPA Janus Benchmark C datasets. Additionally, the authors have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal FR. The authors have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, the authors have also evaluated the authors’ cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset.https://doi.org/10.1049/bme2.12069biometric applicationsface biometricsface recognitionfeature extractionimage retrieval |
| spellingShingle | Fariborz Taherkhani Veeru Talreja Jeremy Dawson Matthew C. Valenti Nasser M. Nasrabadi Profile to frontal face recognition in the wild using coupled conditional generative adversarial network IET Biometrics biometric applications face biometrics face recognition feature extraction image retrieval |
| title | Profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| title_full | Profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| title_fullStr | Profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| title_full_unstemmed | Profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| title_short | Profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| title_sort | profile to frontal face recognition in the wild using coupled conditional generative adversarial network |
| topic | biometric applications face biometrics face recognition feature extraction image retrieval |
| url | https://doi.org/10.1049/bme2.12069 |
| work_keys_str_mv | AT fariborztaherkhani profiletofrontalfacerecognitioninthewildusingcoupledconditionalgenerativeadversarialnetwork AT veerutalreja profiletofrontalfacerecognitioninthewildusingcoupledconditionalgenerativeadversarialnetwork AT jeremydawson profiletofrontalfacerecognitioninthewildusingcoupledconditionalgenerativeadversarialnetwork AT matthewcvalenti profiletofrontalfacerecognitioninthewildusingcoupledconditionalgenerativeadversarialnetwork AT nassermnasrabadi profiletofrontalfacerecognitioninthewildusingcoupledconditionalgenerativeadversarialnetwork |