Relational Deep Feature Learning for Heterogeneous Face Recognition

Cho, MyeongAh; Kim, Taeoh; Kim, Ig-Jae; Lee, Kyung-Jae; Lee, Sangyoun

doi:10.1109/tifs.2020.3013186

Cited by 36 publications

(19 citation statements)

References 47 publications

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“…We compared the our method with other deep learning methods, including TRIVET [19], IDR [20], ADFL [4], CDL [21], WCNN [7], RM [13], and RGM [16]. In Table 2, our PRAM performed better than the RM, which pairwise concatenated the feature vector with the addition of conditional triplet loss (L C ).…”

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

“…In Table 3, our approach exhibits the best performance Models CASIA NIR-VIS 2.0 [1] Rank-1 Acc. (%) VR@FAR=0.1%(%) TRIVET [19] 95.7 78 IDR [20] 97.33 95.73 ADFL [4] 98.15 97.21 CDL [21] 98.62 98.32 WCNN [7] 98.7 98.4 RM [13] 94.73 94.31 RGM [16] 97 TRIVET [19] 93.9 80.9 IDR [20] 94.3 84.7 ADFL [4] 95.2 95.3 CDL [21] 96.9 95.9 WCNN [7] 97.4 96 RGM [16] 97. on the BUAA-VisNir database with a large variance in emotion and pose. Compared to the WCNN and ADFL, our method is slightly lower in the CASIA NIR-VIS 2.0 database but still demonstrate competitive performance, and higher performance with 2.04% and 4.24% in Buaa database.…”

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

“…As for the loss value L i C in Eq. 2, we use the triplet loss with a conditional margin proposed by [16] where CS represents the cosine similarity, and the conditional margin m is set to 0.55. The x i indicates feature vector of each component, and a, p, n refer to anchor, positive and negative example, respectively.…”

Section: Component Adaptive Triplet Loss Functionmentioning

confidence: 99%

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A Heterogeneous Face Recognition Via Part Adaptive And Relation Attention Module

Cho

Lee

2021

2021 IEEE International Conference on Image Processing (ICIP)

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In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The illumination difference between NIR and visible-light (VIS) images causes a domain gap, and the variations in pose and emotion also make facial matching more difficult. Since heterogeneous face recognition (HFR) has difficulties in domain discrepancy, many studies have focused on extracting domaininvariant features, such as facial part relational information. However, when pose variation occurs, the facial component position changes and a different part relation is extracted. In this paper, we propose a part relation attention module that crops facial parts obtained through a semantic mask and performs relational modeling using each of these representative features. Furthermore, we suggest component adaptive triplet loss using adaptive weights for each part to reduce the intraclass distance regardless of the domain as well as pose. Finally, our method exhibits a performance improvement in the CASIA NIR-VIS 2.0 [1] and achieves superior results in the BUAA-VisNir [2] with large pose and emotion variations.

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Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

Section: Component Adaptive Triplet Loss Functionmentioning

confidence: 99%

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A Heterogeneous Face Recognition Via Part Adaptive And Relation Attention Module

Cho

Lee

2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…Heterogeneity invariant feature-based approaches focus on extracting features which are invariant across different views. The prominent research includes use of handcrafted features such as variants of histogram of oriented gradients (HOG), Gabor, Weber, local binary patterns (LBP) (Liao et al, 2009;Goswami et al, 2011;Kalka et al, 2011;Chen and Ross, 2013;Dhamecha et al, 2014), and various learning-based features (Yi et al, 2015;Liu et al, 2016;Reale et al, 2016;He et al, 2017;Hu et al, 2018;Cho et al, 2020). Heterogeneity-aware classifier-based approaches focus on learning a model using samples from both the views.…”

Section: Literature Reviewmentioning

confidence: 99%

Kernelized Heterogeneity-Aware Cross-View Face Recognition

Dhamecha¹,

Ghosh²,

Vatsa³

et al. 2021

Front. Artif. Intell.

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Cross-view or heterogeneous face matching involves comparing two different views of the face modality such as two different spectrums or resolutions. In this research, we present two heterogeneity-aware subspace techniques, heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) that encode heterogeneity in the objective function and yield a suitable projection space for improved performance. They can be applied on any feature to make it heterogeneity invariant. We next propose a face recognition framework that uses existing facial features along with HDA/KHDA for matching. The effectiveness of HDA and KHDA is demonstrated using both handcrafted and learned representations on three challenging heterogeneous cross-view face recognition scenarios: (i) visible to near-infrared matching, (ii) cross-resolution matching, and (iii) digital photo to composite sketch matching. It is observed that, consistently in all the case studies, HDA and KHDA help to reduce the heterogeneity variance, clearly evidenced in the improved results. Comparison with recent heterogeneous matching algorithms shows that HDA- and KHDA-based matching yields state-of-the-art or comparable results on all three case studies. The proposed algorithms yield the best rank-1 accuracy of 99.4% on the CASIA NIR-VIS 2.0 database, up to 100% on the CMU Multi-PIE for different resolutions, and 95.2% rank-10 accuracies on the e-PRIP database for digital to composite sketch matching.

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“…This work has some resemblance to face authentication, e.g., regarding feature extraction. The features are often extracted through multiple hidden layers of deep (convolutional) neural networks and contain representative information that is used to distinguish an individual [9]. Recent work in this domain proposed an adaptive curriculum learning loss (called CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage [20].…”

Section: Related Workmentioning

confidence: 99%

Evaluating facial recognition services as interaction technique for recommender systems

Pessemier

Coppens

Martens

2020

Multimed Tools Appl

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Recommender systems are tools and techniques to assist users in the content selection process thereby coping with the problem of information overload. For recommender systems, user authentication and feedback gathering are of crucial importance. However, the typical user authentication with username / password and feedback method with a star rating system are not user friendly and often bypassed. This article proposes an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics such as emotions. We studied the use case of video watching. Photos made with the front-facing camera of a tablet, smartphone, or smart TV are used as input of a facial recognition service. The persons in front of the screen can be identified. During video watching, implicit feedback for the video content is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An evaluation with a test panel showed that the recognized emotions are correlated with the user's star ratings and that happiness can be most accurately detected. So as the main contribution, this article indicates that emotion recognition might be used as an alternative feedback mechanism for recommender systems.

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Relational Deep Feature Learning for Heterogeneous Face Recognition

Cited by 36 publications

References 47 publications

A Heterogeneous Face Recognition Via Part Adaptive And Relation Attention Module

A Heterogeneous Face Recognition Via Part Adaptive And Relation Attention Module

Kernelized Heterogeneity-Aware Cross-View Face Recognition

Evaluating facial recognition services as interaction technique for recommender systems

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