An improved age invariant face recognition using data augmentation

Okokpujie, Kennedy; John, Samuel; Ndujiuba, Charles Uzoanya; Badejo, Joke A.; Osaghae, Etinosa Noma

doi:10.11591/eei.v10i1.2356

Cited by 14 publications

(11 citation statements)

References 43 publications

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“…ResNet50, which won the ILSVRC-2015 competition in 2015, is an architecture meant to deal with the problem of several nonlinear layers not learning identity maps and deterioration. ResNet50 created residual connections between layers, reducing loss, preserving knowledge gain, and improving training performance [32,33]. If a layer has a residual connection, its output is a convolution of its input plus its input.…”

Section: Resnet50mentioning

confidence: 99%

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

Okokpujie¹,

Okokpujie²,

Ayomikun³

et al. 2023

MMEP

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Cassava is one of the six food items identified as a critical food product for Africa, owing to its importance to African farmers' lives and ability to alter African economies. However, Cassava plant diseases have affected the yield of farmers significantly which has led to a decline in the agricultural production of cassava. Therefore, the aim of this research work is to develop a web and mobile applications-based system that would be able to detect cassava diseases based on its leaf images. To achieve this aim, pre-trained Convolutional Neural Network (CNN) models were selected using their previous performance and the application of transfer learning technique, new models were developed to classify cassava diseases based on the dataset curated and pre-processed. The best three models were selected: MobileNetV2, VGG16 and ResNet50. After training, the accuracy for each model was: 98%, 92% and 75% for MobileNetV2, VGG16 and ResNet50 respectively. Following evaluation of performance, the model with the best accuracy (MobileNetV2) was deployed using a web application interface. After deploying as a web and mobile apps interface, it was further tested to see how it would perform on the field. This research work was found capable of aiding farmers in being able to timely detect the type of disease affecting their cassava plants and the correct treatment to utilize; this also contribute towards Sustainable development goals (SDG) 2 and 12.

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Section: Resnet50mentioning

confidence: 99%

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

Okokpujie¹,

Okokpujie²,

Ayomikun³

et al. 2023

MMEP

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“…This made the data for ageinvariant facial recognition extremely baffling. Additionally, hats, facial hair, and eyeglasses served as occlusions in a number of images [16].…”

Section: Complexities and Specifications Of The Datasetmentioning

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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“…Image threshold and preprocessing techniques like enhancement, thinning for binary image, is required for this application in order to remove noise effect and to simplify the test image processing. Preprocessing steps include several traditional image processing methods which are applied together to obtain a better input data for features extraction step [11], [34], [40], [58], [70], [83], [101]- [103].…”

Section: Features Extractionmentioning

confidence: 99%

User identification system for inked fingerprint pattern based on central moments

Baker

Alazawi

Ahmed

et al. 2021

IJEECS

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The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

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An improved age invariant face recognition using data augmentation

Cited by 14 publications

References 43 publications

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

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

User identification system for inked fingerprint pattern based on central moments

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