Age Invariant Face Recognition using Convolutional  Neural Network

Nimbarte, Mrudula; Bhoyar, Kishor K.

doi:10.11591/ijece.v8i4.pp2126-2138

Cited by 15 publications

(10 citation statements)

References 30 publications

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“…For a database of size , LOPO runs a number of experiments, on each uses images for training and only one image for testing. The LOPO scheme, used for AIFR throughout literature (e.g., in [10,19,29]), is usually used in forensic applications and missed person identification, where one image of the person at a certain age is available and it is required to retrieve the correct person identity from the aging database. KNN classifier [28] is used for matching with .…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we employed the VGG-face CNN model, with two major contributions:  The proposed method involves no preprocessing steps, i.e., images are only resized to the standard size of VGG-model input without any form of registration. Registration steps, usually used in the literature [13,[17][18][19], may not only involve alignment errors [20,21], but also make the method less robust, time consuming, and sensitive to the success of the registration step.  The extracted features using the transfer learning VGG-model are further optimized using a sophisticated GA in order to select the most relevant features to AIFR.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of deep learning features for age-invariant face recognition

Moustafa¹,

Elnakib²,

Areed³

2020

IJECE

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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, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of deep learning features for age-invariant face recognition

Moustafa¹,

Elnakib²,

Areed³

2020

IJECE

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“…Another study has 98.3% of accuracy [16]. Furthermore, [17] recognize a face with a 92.5 percent accuracy rating. CNN appears to be accurate for facial recognition.…”

Section: Article Historymentioning

confidence: 99%

Convolutional Neural Network (CNN) to determine the character of wayang kulit

Wibawa

Pratama

Handayani

et al. 2021

Int. J. Vis. Perform. Arts

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Indonesia is a country with diverse cultures. One of which is Wayang Kulit, which has been recognized by UNESCO. Wayang kulit has a variety of names and personalities, however most younger generations are not familiar with the characters of these shadow puppets. With today's rapid technological advancements, people could use this technology to detect objects using cameras. Convolutional Neural Network (CNN) is one method that can be used. CNN is a learning process that is included in the Deep Learning section and is used to find the best representation. The CNN is commonly used for object detection, would be used to classify good and bad characters. The data used consists of 100 black and white puppet images that were downloaded one at a time. The data was obtained through a training process that uses the CNN method and Google Colab to help speed up the training process. After that, a new model is created to test the puppet images. The result obtained a 92 percent accuracy rate, means that CNN can differentiate the Wayang Kulit character

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“…Artificial neural networks has been the most popular tools for machine learning [4], which in more general sense for deep learning. Among several deep learning architectures, stacked denoising autoencoders [5], deep belief networks [6][7], and convolutional neural networks [8][9][10][11][12][13] are three of the most popular architectures utilized for different type of applications. Convolutional neural networks (CNNs) are a special kind of deep learning method, CNNs run much faster on GPUs, such as NVidia's Tesla K80 processor, and has achieved state of the art performance on various computer vision tasks, such as object detection, recognition, retrieval, annotation, image classification, and segmentation [14][15][16].…”

Section: Convolutional Neural Network (Cnns) For Nr-iqamentioning

confidence: 99%

Improve of contrast-distorted image quality assessment based on convolutional neural networks

Ahmed

Chen

Jamil

et al. 2019

IJECE

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<span lang="EN-US">Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI.</span>

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Age Invariant Face Recognition using Convolutional Neural Network

Cited by 15 publications

References 30 publications

Optimization of deep learning features for age-invariant face recognition

Optimization of deep learning features for age-invariant face recognition

Convolutional Neural Network (CNN) to determine the character of wayang kulit

Improve of contrast-distorted image quality assessment based on convolutional neural networks

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