SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms

Alonso‐Fernandez, Fernando; Barrachina, Javier; Hernandez‐Diaz, Kevin; Bigün, Josef

doi:10.1007/978-3-030-68793-9_10

“…In contrast, end-to-end training of the networks on the target domain is evaluated here too. Also, the present study evaluates networks pre-trained in a related task, face recognition [6,38], where large databases are available. Since both tasks use the same type of input data, we aim at analyzing if such face recognition pre-training can be beneficial for soft-biometrics.…”

Section: Contributionsmentioning

confidence: 99%

“…This demands architectures capable of working in mobile devices, a constraint not considered in our previous study. The lighter CNNs that we employ [39,40] have been proposed for common visual tasks in the context of the ImageNet challenge, and they have been bench-marked for face recognition as well [6,41,42]. To achieve less parameters and faster processing while keeping accuracy, they use techniques such as point-wise convolution, depth-wise separable convolution, bottleneck layers, or residual connections [40].…”

Section: Contributionsmentioning

confidence: 99%

“…It allows to use the network as feature extractor and simply train a classifier, or to facilitate end-to-end training if there is few data in the target domain [58]. Due to previous research [6,38], the CNNs are also available after being fine-tuned for face recognition with two large databases [38,60]. Even if face recognition is a different task, we hypothesize that such fine-tuning can be beneficial for soft-biometrics classification.…”

Section: Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

Alonso‐Fernandez¹,

Diaz²,

Perales³

et al. 2021

Preprint

Self Cite

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We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for soft-biometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pre-trained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesize that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pre-training over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition.

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“…It allows to use the network as feature extractor and simply train a classifier, or to facilitate end‐to‐end training if there is little data in the target domain [58]. Due to previous research [6, 52], the CNNs are also available after being fine‐tuned for face recognition with two large databases [52, 60]. Even if face recognition is a different task, we hypothesise that such fine‐tuning can be beneficial for soft‐biometrics classification.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, this work is concerned with the challenge of estimating soft-biometrics when only the ocular region is available. Additionally, we are interested in mobile environments [6]. The pandemic has accelerated the migration to the digital domain, converting mobiles in data hubs used for all type of This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.…”

mentioning

confidence: 99%

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

Alonso‐Fernandez

¹

,

Hernandez‐Diaz

²

,

Perales

³

et al. 2021

Self Cite

View full text Add to dashboard Cite

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for softbiometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pretrained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesise that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pretraining over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition. | INTRODUCTIONRecent research has explored the automatic extraction of information such as gender, age, ethnicity, etc. of an individual, known as soft-biometrics [1]. It can be deduced from biometric data like face photos, voice, gait, hand or body images, etc. One of the most natural ways is face analysis [2], but given the use of masks due to the COVID-19 pandemic, the face appears occluded even in cooperative settings, leaving the ocular region as the only visible part. In recent years, the ocular region has gained attention as a stand-alone modality for a variety of tasks, including person recognition [3], softbiometrics estimation [4], or liveness detection [5]. Accordingly, this work is concerned with the challenge of estimating soft-biometrics when only the ocular region is available. Additionally, we are interested in mobile environments [6]. The pandemic has accelerated the migration to the digital domain, converting mobiles in data hubs used for all type of This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More via Filter Pruning

Alonso-Fernandez,

Hernandez-Diaz,

Buades Rubio

et al. 2023

Lecture Notes in Computer Science

1

0

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SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms

Cited by 8 publications

References 20 publications

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More via Filter Pruning

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