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Alonso-Fernandez, F., Hernandez-Diaz, K., Buades Rubio, J. M. & Bigun, J. (2024). SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning. In: Hernández Heredia, Y.; Milián Núñez, V.; Ruiz Shulcloper, J. (Ed.), Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023.: . Paper presented at VIII International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR, Varadero, Cuba, September 27-29, 2023 (pp. 349-361). Cham: Springer, 14335
Open this publication in new window or tab >>SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning
2024 (English)In: Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. / [ed] Hernández Heredia, Y.; Milián Núñez, V.; Ruiz Shulcloper, J., Cham: Springer, 2024, Vol. 14335, p. 349-361Conference paper, Published paper (Refereed)
Abstract [en]

The widespread use of mobile devices for various digital services has created a need for reliable and real-time person authentication. In this context, facial recognition technologies have emerged as a dependable method for verifying users due to the prevalence of cameras in mobile devices and their integration into everyday applications. The rapid advancement of deep Convolutional Neural Networks (CNNs) has led to numerous face verification architectures. However, these models are often large and impractical for mobile applications, reaching sizes of hundreds of megabytes with millions of parameters. We address this issue by developing SqueezerFaceNet, a light face recognition network which less than 1M parameters. This is achieved by applying a network pruning method based on Taylor scores, where filters with small importance scores are removed iteratively. Starting from an already small network (of 1.24M) based on SqueezeNet, we show that it can be further reduced (up to 40%) without an appreciable loss in performance. To the best of our knowledge, we are the first to evaluate network pruning methods for the task of face recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Cham: Springer, 2024
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14335
Keywords
Face recognition, Mobile Biometrics, CNN pruning, Taylor scores
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-51299 (URN)10.1007/978-3-031-49552-6_30 (DOI)2-s2.0-85180788350 (Scopus ID)978-3-031-49551-9 (ISBN)978-3-031-49552-6 (ISBN)
Conference
VIII International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR, Varadero, Cuba, September 27-29, 2023
Funder
Swedish Research CouncilVinnova
Note

Funding: F. A.-F., K. H.-D., and J. B. thank the Swedish Research Council (VR) and the Swedish Innovation Agency (VINNOVA) for funding their research. Author J. M. B. thanks the project EX-PLAINING - "Project EXPLainable Artificial INtelligence systems for health and well-beING", under Spanish national projects funding (PID2019-104829RA-I00/AEI/10.13039/501100011033).

Available from: 2023-07-20 Created: 2023-07-20 Last updated: 2024-01-18Bibliographically approved
Alonso-Fernandez, F., Hernandez-Diaz, K., Buades, J. M., Tiwari, P. & Bigun, J. (2023). An Explainable Model-Agnostic Algorithm for CNN-Based Biometrics Verification. In: 2023 IEEE International Workshop on Information Forensics and Security (WIFS): . Paper presented at 2023 IEEE International Workshop on Information Forensics and Security, WIFS 2023, Nürnberg, Germany, 4-7 December, 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>An Explainable Model-Agnostic Algorithm for CNN-Based Biometrics Verification
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2023 (English)In: 2023 IEEE International Workshop on Information Forensics and Security (WIFS), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Biometrics, Explainable AI, Face recognition, XAI
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-52721 (URN)10.1109/WIFS58808.2023.10374866 (DOI)2-s2.0-85183463933 (Scopus ID)9798350324914 (ISBN)
Conference
2023 IEEE International Workshop on Information Forensics and Security, WIFS 2023, Nürnberg, Germany, 4-7 December, 2023
Projects
EXPLAINING - ”Project EXPLainable Artificial INtelligence systems for health and well-beING”
Funder
Swedish Research CouncilVinnova
Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2024-02-16Bibliographically approved
Busch, C., Deravi, F., Frings, D., Alonso-Fernandez, F. & Bigun, J. (2023). Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics. IET Biometrics, 12(2), 112-128
Open this publication in new window or tab >>Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics
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2023 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 12, no 2, p. 112-128Article in journal (Refereed) Published
Abstract [en]

Due to migration, terror-threats and the viral pandemic, various EU member states have re-established internal border control or even closed their borders. European Association for Biometrics (EAB), a non-profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re-establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re-establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade-off with regards to open borders while maintaining a high-level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions. © 2023 The Authors. IET Biometrics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Place, publisher, year, edition, pages
Oxford: John Wiley & Sons, 2023
Keywords
biometric applications, biometric template protection, biometrics (access control), computer vision, data privacy, image analysis for biometrics, object tracking
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-50367 (URN)10.1049/bme2.12107 (DOI)000976420600001 ()2-s2.0-85153281620 (Scopus ID)
Available from: 2023-04-20 Created: 2023-04-20 Last updated: 2023-12-06Bibliographically approved
Hernandez-Diaz, K., Alonso-Fernandez, F. & Bigun, J. (2023). One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations. IEEE Access, 11, 100396-100413
Open this publication in new window or tab >>One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 100396-100413Article in journal (Refereed) Published
Abstract [en]

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers’ output for unseen data and evaluate the method’s robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70%and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2023
Keywords
Biometrics, Biometrics (access control), Databases, Deep learning, Deep Representation, Face recognition, Feature extraction, Image recognition, Iris recognition, One-Shot Learning, Periocular, Representation learning, Task analysis, Training, Transfer Learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-51749 (URN)10.1109/ACCESS.2023.3315234 (DOI)2-s2.0-85171525429 (Scopus ID)
Funder
Swedish Research CouncilVinnova
Available from: 2023-10-19 Created: 2023-10-19 Last updated: 2023-10-20Bibliographically approved
Karlsson, J., Strand, F., Bigun, J., Alonso-Fernandez, F., Hernandez-Diaz, K. & Nilsson, F. (2023). Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers. In: Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred (Ed.), Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods: February 22-24, 2023, in Lisbon, Portugal. Paper presented at 12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023 (pp. 395-402). SciTePress, 1
Open this publication in new window or tab >>Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers
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2023 (English)In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods: February 22-24, 2023, in Lisbon, Portugal / [ed] Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred, SciTePress, 2023, Vol. 1, p. 395-402Conference paper, Published paper (Refereed)
Abstract [en]

Workplace injuries are common in today’s society due to a lack of adequately worn safety equipment. A system that only admits appropriately equipped personnel can be created to improve working conditions. The goal is thus to develop a system that will improve workers’ safety using a camera that will detect the usage of Personal Protective Equipment (PPE). To this end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system into an entry control point where workers must present themselves to obtain access to a restricted area. Combined with facial identity recognition, the system would ensure that only authorized people wearing appropriate equipment are granted access. A novelty of this work is that we increase the number of classes to five objects (hardhat, safety vest, safety gloves, safety glasses, and hearing protection), whereas most existing works only focus on one or two classes, usually hardhats or vests. The AI model developed provides good detection accuracy at a distance of 3 and 5 meters in the collaborative environment where we aim at operating (mAP of 99/89%, respectively). The small size of some objects or the potential occlusion by body parts have been identified as potential factors that are detrimental to accuracy, which we have counteracted via data augmentation and cropping of the body before applying PPE detection. © 2023 by SCITEPRESS-Science and Technology Publications, Lda.

Place, publisher, year, edition, pages
SciTePress, 2023
Series
ICPRAM, E-ISSN 2184-4313
Keywords
PPE, PPE Detection, Personal Protective Equipment, Machine Learning, Computer Vision, YOLO
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-48795 (URN)10.5220/0011693500003411 (DOI)2-s2.0-85174511525 (Scopus ID)978-989-758-626-2 (ISBN)
Conference
12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023
Projects
2021-05038 Vinnova DIFFUSE Disentanglement of Features For Utilization in Systematic Evaluation
Available from: 2022-12-09 Created: 2022-12-09 Last updated: 2023-11-30Bibliographically approved
Alonso-Fernandez, F. & Bigun, J. (2022). Continuous Examination by Automatic Quiz Assessment Using Spiral Codes and Image Processing. In: Ilhem Kallel; Habib M. Kammoun; Lobna Hsairi (Ed.), 2022 IEEE Global Engineering Education Conference (EDUCON): . Paper presented at 13th IEEE Global Engineering Education Conference, EDUCON (Educational Conference), Tunis, Tunisia, 28-31 March, 2022 (pp. 929-935). IEEE, 2022-Marc
Open this publication in new window or tab >>Continuous Examination by Automatic Quiz Assessment Using Spiral Codes and Image Processing
2022 (English)In: 2022 IEEE Global Engineering Education Conference (EDUCON) / [ed] Ilhem Kallel; Habib M. Kammoun; Lobna Hsairi, IEEE, 2022, Vol. 2022-Marc, p. 929-935Conference paper, Published paper (Refereed)
Abstract [en]

We describe a technical solution implemented at Halmstad University to automatise assessment and reporting of results of paper-based quiz exams. Paper quizzes are affordable and within reach of campus education in classrooms. Offering and taking them is accepted as they cause fewer issues with reliability and democratic access, e.g. a large number of students can take them without a trusted mobile device, internet, or battery. By contrast, correction of the quiz is a considerable obstacle. We suggest mitigating the issue by a novel image processing technique using harmonic spirals that aligns answer sheets in sub-pixel accuracy to read student identity and answers and to email results within minutes, all fully automatically. Using the described method, we carry out regular weekly examinations in two master courses at the mentioned centre without a significant workload increase. The employed solution also enables us to assign a unique identifier to each quiz (e.g. week 1, week 2...) while allowing us to have an individualised quiz for each student. © 2022 IEEE.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE Global Engineering Education Conference, ISSN 2165-9559, E-ISSN 2165-9567 ; 2022
Keywords
Continuous examination, automatic correction, image processing, spiral codes, continuous education
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-46251 (URN)10.1109/EDUCON52537.2022.9766699 (DOI)000836390500137 ()2-s2.0-85123685725 (Scopus ID)978-1-6654-4434-7 (ISBN)978-1-6654-4435-4 (ISBN)
Conference
13th IEEE Global Engineering Education Conference, EDUCON (Educational Conference), Tunis, Tunisia, 28-31 March, 2022
Funder
Swedish Research CouncilVinnovaKnowledge Foundation
Available from: 2022-01-26 Created: 2022-01-26 Last updated: 2023-10-05Bibliographically approved
Alonso-Fernandez, F., Raja, K. B., Raghavendra, R., Busch, C., Bigun, J., Vera-Rodriguez, R. & Fierrez, J. (2022). Cross-sensor periocular biometrics in a global pandemic: Comparative benchmark and novel multialgorithmic approach. Information Fusion, 83-84, 110-130
Open this publication in new window or tab >>Cross-sensor periocular biometrics in a global pandemic: Comparative benchmark and novel multialgorithmic approach
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2022 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 83-84, p. 110-130Article in journal (Refereed) Published
Abstract [en]

The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and a significant performance drop if images from heterogeneous environments are compared for person recognition purposes. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear or to exchange information between agencies or applications operating in different environments. Furthermore, variations in imaging spectral bands can also occur. For example, face images are typically acquired in the visible (VIS) spectrum, while iris images are usually captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if, for example, a face image obtained from a surveillance camera needs to be compared against a legacy database of iris imagery. Here, we propose a multialgorithmic approach to cope with periocular images captured with different sensors. With face masks in the front line to fight against the COVID-19 pandemic, periocular recognition is regaining popularity since it is the only region of the face that remains visible. As a solution to the mentioned cross-sensor issues, we integrate different biometric comparators using a score fusion scheme based on linear logistic regression This approach is trained to improve the discriminating ability and, at the same time, to encourage that fused scores are represented by log-likelihood ratios. This allows easy interpretation of output scores and the use of Bayes thresholds for optimal decision-making since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the 1st Cross-Spectral Iris/Periocular Competition, whose aim was to compare person recognition approaches when periocular data from visible and near-infrared images is matched. The proposed fusion approach achieves reductions in the error rates of up to 30%–40% in cross-spectral NIR–VIS comparisons with respect to the best individual system, leading to an EER of 0.2% and a FRR of just 0.47% at FAR = 0.01%. It also represents the best overall approach of the mentioned competition. Experiments are also reported with a database of VIS images from two different smartphones as well, achieving even bigger relative improvements and similar performance numbers. We also discuss the proposed approach from the point of view of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches in multibiometrics, such as the average of scores, Support Vector Machines, or Random Forest. © 2022 The Authors

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2022
Keywords
Cross-sensor, Cross-spectral, Linear logistic regression, Multibiometrics fusion, Ocular biometrics, Periocular recognition, Sensor interoperability
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-46748 (URN)10.1016/j.inffus.2022.03.008 (DOI)000794868000003 ()2-s2.0-85127807945 (Scopus ID)
Funder
Swedish Research Council, 2016-03497Knowledge FoundationVinnova, 2018-00472
Note

Funding: Part of this work was done while F. A.-F. was a visiting researcher at the Norwegian University of Science and Technology in Gjøvik (Norway), funded by EU COSTAction IC1106. Authors from HH thank the Swedish Research Council (project 2016-03497), the Swedish Knowledge Foundation (CAISR and SIDUS-AIR Program), and the Swedish Innovation Agency VINNOVA (project 2018-00472) for funding his research. Authors from UAM are funded by projects: PRIMA (MSCA-ITN-2019-860315), TRESPASS-ETN (MSCA-ITN-2019-860813), and BIBECA (RTI2018-101248-B-I00 MINECO).

Available from: 2022-05-03 Created: 2022-05-03 Last updated: 2023-08-21Bibliographically approved
Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J. & Alonso-Fernandez, F. (2022). On the effect of selfie beautification filters on face detection and recognition. Pattern Recognition Letters, 163, 104-111
Open this publication in new window or tab >>On the effect of selfie beautification filters on face detection and recognition
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2022 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 163, p. 104-111Article in journal (Refereed) Published
Abstract [en]

Beautification and augmented reality filters are very popular in applications that use selfie images. However, they can distort or modify biometric features, severely affecting the ability to recognise the individuals’ identity or even detect the face. Accordingly, we address the effect of such filters on the accuracy of automated face detection and recognition. The social media image filters studied modify the image contrast, illumination, or occlude parts of the face. We observe that the effect of some of these filters is harmful to face detection and identity recognition, especially if they obfuscate the eye or (to a lesser extent) the nose. To counteract such effect, we develop a method to reverse the applied manipulation with a modified version of the U-NET segmentation network. This method is observed to contribute to better face detection and recognition accuracy. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using several CNN backbones. We also evaluate if incorporating filtered images into the training set of machine learning approaches is beneficial. Our results show good recognition when filters do not occlude important landmarks, especially the eyes. The combined effect of the proposed approaches also allows mitigating the impact produced by filters that occlude parts of the face. © 2022 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2022
Keywords
Face detection, Face recognition, Social media filters, Beautification, U-NET, Convolutional neural network
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-48481 (URN)10.1016/j.patrec.2022.09.018 (DOI)000877215000005 ()2-s2.0-85139592291 (Scopus ID)
Funder
Swedish Research CouncilVinnova
Available from: 2022-10-14 Created: 2022-10-14 Last updated: 2023-08-21Bibliographically approved
Hagström, A. L., Stanikzai, R., Bigun, J. & Alonso-Fernandez, F. (2022). Writer Recognition Using Off-line Handwritten Single Block Characters. In: : . Paper presented at 10th International Workshop on Biometrics and Forensics, IWBF, Salzburg, Austria, April 20-21, 2022. IEEE
Open this publication in new window or tab >>Writer Recognition Using Off-line Handwritten Single Block Characters
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Block characters are often used when filling paper forms for a variety of purposes. We investigate if there is biometric information contained within individual digits of handwritten text. In particular, we use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition approaches, one based on handcrafted features that compute contour directional measurements, and another based on deep features from a ResNet50 model. We use a self-captured database of 317 individuals and 4920 written DoBs in total. Results show the presence of identity-related information in a piece of handwritten information as small as six digits with the DoB. We also analyze the impact of the amount of enrolment samples, varying its number between one and ten. Results with such small amount of data are promising. With ten enrolment samples, the Top-1 accuracy with deep features is around 94%, and reaches nearly 100% by Top-10. The verification accuracy is more modest, with EER>20% with any given feature and enrolment set size, showing that there is still room for improvement.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Off-line writer recognition, writer identification, writer verification, biometrics
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-46443 (URN)10.1109/IWBF55382.2022.9794466 (DOI)000850371800001 ()2-s2.0-85133489369 (Scopus ID)9781665469623 (ISBN)
Conference
10th International Workshop on Biometrics and Forensics, IWBF, Salzburg, Austria, April 20-21, 2022
Funder
Swedish Research CouncilVinnovaKnowledge Foundation
Available from: 2022-03-08 Created: 2022-03-08 Last updated: 2023-10-05Bibliographically approved
Alonso-Fernandez, F., Hernandez-Diaz, K., Ramis, S., Perales, F. J. & Bigun, J. (2021). Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images. IET Biometrics, 10(5), 562-580
Open this publication in new window or tab >>Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images
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2021 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 10, no 5, p. 562-580Article in journal (Refereed) Published
Abstract [en]

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. © The Authors

Place, publisher, year, edition, pages
Stevenage: Institution of Engineering and Technology, 2021
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-44262 (URN)10.1049/bme2.12046 (DOI)000661520100001 ()2-s2.0-85108707251 (Scopus ID)
Funder
Swedish Research Council, 2016-03497
Note

Funding: Part of this research has been enabled by a visiting position of F. Alonso-Fernandez at the University of the Balearic Islands (UIB), funded by the UIB visiting lecturers program. Authors F. Alonso-Fernandez, K. Hernandez-Diaz and J. Bigun would like to thank the Swedish Research Council for funding their research. Authors F. J. Perales and S. Ramis would like to thank the projects PERGAMEX RTI2018-096986-B-C31 (MINECO/AEI/ ERDF, EU) and PID2019-104829RA-I00 / AEI / 10.13039/501100011033 (MICINN).

Available from: 2021-05-04 Created: 2021-05-04 Last updated: 2023-06-08Bibliographically approved
Projects
Lip-motion, face and speech analysis in synergy, for human-machine interfaces [2008-03876_VR]; Halmstad UniversityScale, orientation and illumination invariant information encoding and decoding-- A study on invariant visual codes [2011-05819_VR]; Halmstad UniversityFacial detection and recognition resilient to physical image deformations [2012-04313_VR]; Halmstad UniversityFacial Analysis in the Era of Mobile Devices and Face Masks [2021-05110_VR]; Halmstad University; Publications
Alonso-Fernandez, F., Hernandez-Diaz, K., Buades Rubio, J. M. & Bigun, J. (2024). SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning. In: Hernández Heredia, Y.; Milián Núñez, V.; Ruiz Shulcloper, J. (Ed.), Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023.: . Paper presented at VIII International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR, Varadero, Cuba, September 27-29, 2023 (pp. 349-361). Cham: Springer, 14335Busch, C., Deravi, F., Frings, D., Alonso-Fernandez, F. & Bigun, J. (2023). Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics. IET Biometrics, 12(2), 112-128Zell, O., Påsson, J., Hernandez-Diaz, K., Alonso-Fernandez, F. & Nilsson, F. (2023). Image-Based Fire Detection in Industrial Environments with YOLOv4. In: : . Paper presented at 12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023. Baaz, A., Yonan, Y., Hernandez-Diaz, K., Alonso-Fernandez, F. & Nilsson, F. (2023). Synthetic Data for Object Classification in Industrial Applications. In: Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred (Ed.), Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM: . Paper presented at 12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023 (pp. 387-394). SciTePress, 1Karlsson, J., Strand, F., Bigun, J., Alonso-Fernandez, F., Hernandez-Diaz, K. & Nilsson, F. (2023). Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers. In: Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred (Ed.), Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods: February 22-24, 2023, in Lisbon, Portugal. Paper presented at 12th International Conference on Pattern Recognition Applications and Methods, ICPRAM, Lisbon, Portugal, February 22-24, 2023 (pp. 395-402). SciTePress, 1Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J. & Alonso-Fernandez, F. (2022). On the effect of selfie beautification filters on face detection and recognition. Pattern Recognition Letters, 163, 104-111
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