Pose Attention-Guided Profile-to-Frontal Face Recognition

Mostofa, Moktari; Ebrahimi, Saadabadi, Mohammad Saeed; Malakshan, Sahar Rahimi; Nasrabadi, Nasser M.

doi:10.1109/ijcb54206.2022.10007935

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…Most of the previous FR methods were established on a metric-learning loss function, such as triplet [32] or contrastive loss [8,30]. These loss functions were based on sample-to-sample comparison in Euclidean space.…”

Section: Fr Loss Functionsmentioning

confidence: 99%

A Quality Aware Sample-to-Sample Comparison for Face Recognition

Ebrahimi¹,

Malakshan²,

Zafari³

et al. 2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Currently available face datasets mainly consist of a large number of high-quality and a small number of lowquality samples. As a result, a Face Recognition (FR) network fails to learn the distribution of low-quality samples since they are less frequent during training (underrepresented). Moreover, current state-of-the-art FR training paradigms are based on the sample-to-center comparison (i.e., Softmax-based classifier), which results in a lack of uniformity between train and test metrics. This work integrates a quality-aware learning process at the sample level into the classification training paradigm (QAFace). In this regard, Softmax centers are adaptively guided to pay more attention to low-quality samples by using a quality-aware function. Accordingly, QAFace adds a quality-based adjustment to the updating procedure of the Softmax-based classifier to improve the performance on the underrepresented low-quality samples. Our method adaptively finds and assigns more attention to the recognizable low-quality samples in the training datasets. In addition, QAFace ignores the unrecognizable low-quality samples using the feature magnitude as a proxy for quality. As a result, QAFace prevents class centers from getting distracted from the optimal direction. The proposed method is superior to the state-ofthe-art algorithms in extensive experimental results on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, IJB-B, and IJB-C datasets.

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Section: Fr Loss Functionsmentioning

confidence: 99%

A Quality Aware Sample-to-Sample Comparison for Face Recognition

Ebrahimi¹,

Malakshan²,

Zafari³

et al. 2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

show abstract

“…However, task-related image quality is the key to boosting the deep model performance [17]. For instance, head pose angle is regarded as an undesirable quality factor for a face recognition network [18], [19]. However, estimating head pose angle is the goal of a HPE algorithm and it is not considered as an undesirable factor of an input image.…”

Section: Introductionmentioning

confidence: 99%

Joint Super-Resolution and Head Pose Estimation for Extreme Low-Resolution Faces

et al. 2023

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State-of-the-art deep learning-based Head Pose Estimation (HPE) techniques have reached spectacular performance on High-Resolution (HR) face images. However, they still fail to achieve expected performance on low-resolution images at large scales. This work presents an end-to-end HPE framework assisted by a Face Super-Resolution (FSR) algorithm. The proposed FSR model is specifically guided to enhance the HPE performance rather than considering FSR as an independent task. To this end, we utilized a Multi-Stage Generative Adversarial Network (MSGAN) which benefit from a pose-aware adversarial loss and head pose estimation feedback to generate super-resolved images that are properly aligned for HPE. Also, we propose a degradation strategy rather than simple down-sampling approach to mimic the diverse properties of real-world Low-Resolution (LR) images. We evaluate the performance of our proposed method on both synthetic and real-world LR datasets and show the superiority of our approach in both visual and HPE metrics on the AFLW2000, BIWI, and WiderFace Datasets.INDEX TERMS Head pose estimation (HPE), face super-resolution (FSR), multi-stage generative adversarial networks (MSGAN), low-resolution (LR) face images.

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