Age Estimation from Face Image using Discrete Cosine Transform Feature and Artificial Neural Network

Prasetyo, Simeon Yuda; Nabiilah, Ghinaa Zain; Izdihar, Zahra Nabila; Nurhasanah,

doi:10.1109/isriti56927.2022.10052812

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The DCT (Discrete Cosine Transform) algorithm is commonly used in image or signal compression [9]. This algorithm converts spatial data into frequency form, processes frequency data, and converts it back into spatial form by inverting the method.…”

Section: Ekstraksi Fitur Dctmentioning

confidence: 99%

Brain Tumor Detection From Mri Images Using Discrete Cosine Transform Features and Extreme Learning Machine

Prasetyo

2023

JIKO

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A brain tumor is an abnormal growth of brain tissue and characterized by excessive cell proliferation in certain parts of the brain. One of the current, reliable technologies that can be used to identify brain tumors is Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are then conventionally monitored and examined by a specialist for the presence of tumors. As the number of people suffering from brain tumors is significantly increasing and their corresponding mortality rate has reached 18,600 by 2021, research on designing more effective and efficient tools to assist medical specialists in identifying brain tumors is considered of great importance. In a previous study, a machine learning-based model demonstrated its ability to detect brain tumors with a classification accuracy of 92%. Several hyperparameters were computationally tested using public MRI datasets to obtain the most reliable detection/binary classification accuracy on MRI brain images. Sophisticated model accuracy was achieved by testing various neuronal units and ELM activation functions, followed by inserting a feature map extracted from the Discrete Cosine Transform (DCT). The model obtained the highest testing accuracy of 95% with several 20 ELM neuron units with a tanh activation function

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Section: Ekstraksi Fitur Dctmentioning

confidence: 99%

Brain Tumor Detection From Mri Images Using Discrete Cosine Transform Features and Extreme Learning Machine

Prasetyo

2023

JIKO

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“…This study makes use of facial picture datasets with ages ranging from 1 to 116. The study's findings produced a model with an MAE of 9,40 on the testing data that can identify and predict ages in the range of 1 to 116 [7].…”

Section: Introductionmentioning

confidence: 99%

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

Muliawan,

Angky,

Prasetyo

2023

E3S Web Conf.

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There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models such as VGG16, VGG19, ResNet50, and Xception. The proposed approach utilizes PSO to optimize the hyperparameters of a custom output layer for age detection in regression. The PSO algorithm searches for the optimal combination of model hyperparameters that minimize the age estimation error. This study shows that fine-tuning a model can lead to improvements in its performance, with the VGG19 model achieving the best performance after fine-tuning. Additionally, the PSO process was able to find sets of hyperparameters that were on par or even better than the initial hyperparameters. The best result can be seen in VGG19 architecture with loss of 86.181, MAE of 6.693, and MAPE of 38.462. Out of the twelve experiments conducted, it was observed that the utilization of Particle Swarm Optimization (PSO) offered distinct advantages in terms of achieving better results for age estimation. However, it is important to note that the execution time for these experiments was considerably longer when employing PSO.

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Age Estimation from Face Image using Discrete Cosine Transform Feature and Artificial Neural Network

Cited by 2 publications

References 19 publications

Brain Tumor Detection From Mri Images Using Discrete Cosine Transform Features and Extreme Learning Machine

Brain Tumor Detection From Mri Images Using Discrete Cosine Transform Features and Extreme Learning Machine

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

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