A Parallel Deep Learning Approach for Age Invariant Face Recognition system

Dharavath, Krishna; Gaini, Amarnath; Adla, Viod

doi:10.1109/iccci54379.2022.9740907

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Data augmentation is the process of adding noise to photos in order to boost the quantities [24]. The idea of data augmentation has a long history in literature and everyday life [25][26][27][28][29][30][31]. As a result, we were able to collect a pre-processed FG-NET dataset that included 5010 total face photos and an average of 60 face images per person.…”

Section: Fg-net Dataset Pre-processingmentioning

confidence: 99%

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Okokpujie,

Subair

et al. 2023

ISI

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The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments.

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Section: Fg-net Dataset Pre-processingmentioning

confidence: 99%

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Okokpujie,

Subair

et al. 2023

ISI

View full text Add to dashboard Cite

show abstract

“…In order to achieve better result in AIFR, deep learning method is integrated with discriminative approach [17]. In deep learning, Convolutional Neural Network (CNN) algorithm plays vital role in recognizing face with different aging images [18]. Large age gap verification is performed by injecting features in deep networks [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature

Kumar¹,

Pavani²

2022

Artificial Intelligence Annual Volume 2022

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Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. At last, we validate performance of our work using seven performance metrics that are Accuracy, Recall, Rank-1 Score, Precision, F-Score, Recognition rate and computation time.

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Adapting face recognition to the masked world: leveraging deep attention networks with a custom dataset

Trivedi,

Goyani

2024

International Journal of Computers and Applications

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A Parallel Deep Learning Approach for Age Invariant Face Recognition system

Cited by 3 publications

References 24 publications

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature

Adapting face recognition to the masked world: leveraging deep attention networks with a custom dataset

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