Discriminative pose-free descriptors for face and object matching

Sanyal, Soubhik; Mudunuri, Sivaram Prasad; Biswas, Soma

doi:10.1016/j.patcog.2017.02.016

Cited by 4 publications

(3 citation statements)

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“…Considering photo realistic and identity-preserving frontal view synthesis, a domain adaptation strategy for pose invariant FR is discussed in Ref. [56][57][58][59][60]. Tran et al [8] propose a GAN framework to rotate a face and disentangle the identity representation by using a given pose code.…”

Section: Face Frontalisationmentioning

confidence: 99%

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Profile to frontal face recognition in the wild using coupled conditional generative adversarial network

et al. 2022

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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.

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Section: Face Frontalisationmentioning

confidence: 99%

“…Considering photo realistic and identity‐preserving frontal view synthesis, a domain adaptation strategy for pose invariant FR is discussed in Ref. [56–60]. Tran et al.…”

Section: Related Workmentioning

confidence: 99%

Profile to frontal face recognition in the wild using coupled conditional generative adversarial network

et al. 2022

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“…Sina Lotfian and Hassan Foroosh are with the Department of Computer Science, University of Central Florida, Orlando, FL, 32816 USA (e-mail: slotfian@Knights.ucf.edu, foroosh@cs.ucf.edu). GUV OSL 3DFREE IICP CCA [106] GMA [105] DPFD [103] Castillo et al [52] Schels et al [104] Ours…”

Section: Introductionmentioning

confidence: 99%

View-invariant object recognition using homography constraints

Lotfian

Foroosh

2017

2017 IEEE International Conference on Image Processing (ICIP)

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Change in viewpoint is one of the major factors for variation in object appearance across different images. Thus, view-invariant object recognition is a challenging and important image understanding task. In this paper, we propose a method that can match objects in images taken under different viewpoints. Unlike most methods in the literature, no restriction on camera orientations or internal camera parameters are imposed and no prior knowledge of 3D structure of the object is required. We prove that when two cameras take pictures of the same object from two different viewing angels, the relationship between every quadruple of points reduces to the special case of homography with two equal eigenvalues. Based on this property, we formulate the problem as an error function that indicates how likely two sets of 2D points are projections of the same set of 3D points under two different cameras. Comprehensive set of experiments were conducted to prove the robustness of the method to noise, and evaluate its performance on real-world applications, such as face and object recognition.

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Pose-robust face recognition with Huffman-LBP enhanced by Divide-and-Rule strategy

Zhou

et al. 2018

Pattern Recognition

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Discriminative pose-free descriptors for face and object matching

Cited by 4 publications

References 50 publications

Profile to frontal face recognition in the wild using coupled conditional generative adversarial network

Profile to frontal face recognition in the wild using coupled conditional generative adversarial network

View-invariant object recognition using homography constraints

Pose-robust face recognition with Huffman-LBP enhanced by Divide-and-Rule strategy

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