Relevant bibliographies by topics / AIFR-Age invariant face recognition

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'AIFR-Age invariant face recognition'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "AIFR-Age invariant face recognition"

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Bhagya Sree, MS S. "Age-Invariant Face Recognition Based on Identity-Age Shared Features." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46250.

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Abstract— Age-invariant face recognition is a challenging task due to the significant facial changes caused by aging. This paper introduces a novel approach based on identity-age shared features, leveraging multi- vision transformers for robust recognition across age variations. Age-invariant face recognition (AIFR) has gained considerable attention due to its crucial role in identity verification across varying age ranges. Traditional convolutional neural networks (CNNs) have been widely employed for AIFR; however, their limitations in capturing long-range dependencies and facial dynamics acr
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Yan, Chenggang, Lixuan Meng, Liang Li, et al. "Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1s (2022): 1–18. http://dx.doi.org/10.1145/3472810.

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Different from general face recognition, age-invariant face recognition (AIFR) aims at matching faces with a big age gap. Previous discriminative methods usually focus on decomposing facial feature into age-related and age-invariant components, which suffer from the loss of facial identity information. In this article, we propose a novel Multi-feature Fusion and Decomposition (MFD) framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants. Specifically, we first sample multiple face images of different ages with the
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Okokpujie, Kennedy, Etinosa Noma-Osaghae, Samuel Ndueso John, Charles Ndujiuba, and Imhade Princess Okokpujie. "Comparative analysis of augmented datasets performances of age invariant face recognition models." Bulletin of Electrical Engineering and Informatics 10, no. 3 (2021): 1356–67. http://dx.doi.org/10.11591/eei.v10i3.3020.

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The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the prep
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Kennedy, Okokpujie, Noma-Osaghae Etinosa, Ndueso John Samuel, Ndujiuba Charles, and Princess Okokpujie Imhade. "Comparative analysis of augmented datasets performances of age invariant face recognition models." Bulletin of Electrical Engineering and Informatics 10, no. 3 (2021): pp. 1356~1367. https://doi.org/10.11591/eei.v10i3.3020.

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The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the prep
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Prathama, V., and G. Thippeswamy. "Age Invariant Face Recognition." International Journal of Trend in Scientific Research and Development 3, no. 4 (2019): 971–76. https://doi.org/10.31142/ijtsrd23572.

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Face recognition across age progression is remains one of the areas most challenging tasks now a days, as the aging process affects both the shape and texture of a face. One possible solution is to apply a probabilistic model to represent a face simultaneously with its identity variable, which is stable through time, and its aging variable, which changes with time. This paper proposes a deep learning and set based approach to the face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to the sets of images belong
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V, Prathama, and Thippeswamy G. "Age Invariant Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 971–76. http://dx.doi.org/10.31142/ijtsrd23572.

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Okokpujie, Kennedy, Samuel John, Charles Ndujiuba, Joke A. Badejo, and Etinosa Noma Osaghae. "An improved age invariant face recognition using data augmentation." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 179–91. http://dx.doi.org/10.11591/eei.v10i1.2356.

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In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging d
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Kennedy, Okokpujie, John Samuel, Ndujiuba Charles, A. Badejo Joke, and Noma-Osaghae Etinosa. "An improved age invariant face recognition using data augmentation." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 179–91. https://doi.org/10.11591/eei.v10i1.2356.

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In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) a
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Moustafa, Amal A., Ahmed Elnakib, and Nihal F. F. Areed. "Optimization of deep learning features for age-invariant face recognition." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1833. http://dx.doi.org/10.11591/ijece.v10i2.pp1833-1841.

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This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation
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Amal, A. Moustafa, Elnakib Ahmed, and F. F. Areed Nihal. "Optimization of deep learning features for age-invariant face recognition." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (2020): 1833–41. https://doi.org/10.11591/ijece.v10i2.pp1833-1841.

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This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation
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Dissertations / Theses on the topic "AIFR-Age invariant face recognition"

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Lee-YungChen and 陳李永. "Age-Variant Face Recognition Scheme Using Scale Invariant Feature Transform and the Probabilistic Neural Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/83926691560305817266.

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碩士<br>國立成功大學<br>電機工程學系碩士在職專班<br>102<br>Facing to the aging variation problem, how to improve the correct recognition rate of an automatic face recognition system is an important issue. Most face recognition studies only focus on aging simulation or age estimation. For face recognition system under age variation, it is possible to effectively design a suitable and efficient performance matching a framework model. This thesis mainly discusses the differences caused by age level using the Scale Invariant Feature Transform (SIFT) algorithm. Because it has a high tolerance of noise characteristics,
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Book chapters on the topic "AIFR-Age invariant face recognition"

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Jain, Shubham, Aditya Nigam, and Phalguni Gupta. "Age-Invariant Face Recognition Using Shape Transformation." In Intelligent Computing Theories. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39479-9_54.

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Jadli, Priyanka Ravidutt, Riya Huddar, Ankita Gothi, and Rupali Kute. "Deep Features for Age-Invariant Face Recognition." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4727-6_33.

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Ali, Amal Seralkhatem Osman, Vijanth Sagayan a/l Asirvadam, Aamir Saeed Malik, and Azrina Aziz. "A Geometrical Approach for Age-Invariant Face Recognition." In Advances in Visual Informatics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02958-0_8.

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Osman Ali, Amal Seralkhatem, Vijanth Sagayan a/l Asirvadam, Aamir Saeed Malik, and Azrina Aziz. "Age-Invariant Face Recognition Technique Using Facial Geometry." In Soft Computing Applications and Intelligent Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40567-9_8.

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Huang, Hai, Senlin Cheng, Zhong Hong, and Liutong Xu. "Label Distribution Learning Based Age-Invariant Face Recognition." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26142-9_19.

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Hast, Anders, Yijie Zhou, Congting Lai, and Ivar Blohm. "Analysis of Age Invariant Face Recognition Efficiency Using Face Feature Vectors." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59057-3_4.

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Bijarnia, Saroj, and Preety Singh. "Age Invariant Face Recognition Using Minimal Geometrical Facial Features." In Advanced Computing and Communication Technologies. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1023-1_7.

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Le Quang, Minh, Mi Ton Nu Quyen, Nguyen Nguyen Lam, Trung Nguyen Quoc, and Vinh Truong Hoang. "Age-Invariant Face Recognition Based on Self-Supervised Learning." In Intelligence of Things: Technologies and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46749-3_2.

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Wang, Yitong, Dihong Gong, Zheng Zhou, et al. "Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01267-0_45.

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Wu, Changhong, and Jianbo Su. "A Unified Framework for Age Invariant Face Recognition and Age Estimation." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6445-6_68.

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Conference papers on the topic "AIFR-Age invariant face recognition"

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Sharif, Mohsin, Muhammad Awais, Abdullah, Muhammad Huzaifa Tariq, Allah Bux Sargano, and Zulfiqar Habib. "Age-Invariant Face Recognition: A Comprehensive Study of Methods, Datasets, and New Algorithm for Immigration Control." In 2024 Horizons of Information Technology and Engineering (HITE). IEEE, 2024. https://doi.org/10.1109/hite63532.2024.10777245.

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Tandon, A., A. Nigam, and P. Gupta. "An efficient age-invariant face recognition." In International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1548.

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Sindhuja, A., S. Devi Mahalakshmi, and K. Vijayalakshmi. "Age invariant face recognition with occlusion." In 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE, 2012. http://dx.doi.org/10.1109/icaccct.2012.6320746.

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Wang, Haoyi, Victor Sanchez, and Chang-Tsun Li. "Cross-Age Contrastive Learning for Age-Invariant Face Recognition." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10445859.

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Pal, Ravi, and Ajai Kumar Gautam. "Age Invariant Face Recognition using multiclass SVM." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253984.

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Mahalingam, Gayathri, and Chandra Kambhamettu. "Age invariant face recognition using graph matching." In 2010 IEEE Fourth International Conference On Biometrics: Theory, Applications And Systems (BTAS). IEEE, 2010. http://dx.doi.org/10.1109/btas.2010.5634496.

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Baruni, Kedimotse, Nthabiseng Mokoena, Mahalingam Veeraragoo, and Ross Holder. "Age Invariant Face Recognition Methods: A Review." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00317.

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RajaBabu, M., K. Srinivas, and H. Ravi Sankar. "Survey on Age Invariant Face Recognition Techniques." In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2022. http://dx.doi.org/10.1109/ic3i56241.2022.10072620.

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Zhou, Huiling, Kwok-Wai Wong, and Kin-Man Lam. "Feature-aging for age-invariant face recognition." In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415454.

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Islam, Khawar, Sujin Lee, Dongil Han, and Hyeonjoon Moon. "Face Recognition Using Shallow Age-Invariant Data." In 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2021. http://dx.doi.org/10.1109/ivcnz54163.2021.9653432.

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