Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Listio, Syntia Widyayuningtias Putri

doi:10.33395/sinkron.v7i4.11887

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Using the NIST dataset and implementing Contrast Limited Adaptive Histogram Equalization (CLAHE) resulted in an accuracy of up to 95.05%. Meanwhile, Listio [12] implemented CNN to detect gender based on eye image. She compared two models of CNN, namely Inception-V3 and MobileNet to analyze a dataset of 1251 male and 1430 female eyes.…”

Section: Literatures Reviewmentioning

confidence: 99%

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Suwarno

2023

SinkrOn

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Fingerprint-based gender classification is beneficial for speeding up the fingerprint identification of criminals, accident victims, and natural disaster victims that are difficult to be recognized based on their physical characteristics. The biggest obstacle to digitally classifying fingerprints is the image's poor quality. Some methods have been developed to improve image quality through various preprocessing, such as noise removal, background segmentation, thinning, and binarization. However, as these processes increase the classification time, some methods have been developed to classify fingerprints without preprocessing. One of them that has shown excellent success is CNN (Convolutional Neural Network). The method does not require preprocessing, but the computation time is very long and requires large amounts of training data. This study proposed a new method that did not need any preprocessing by using wavelet decomposition combined with the max-pooling process to generate features. Firstly, the fingerprint image was decomposed with a Haar wavelet of 4 levels, and each level was followed by a max-pooling process with a 2´2 filter. After that, the resulting feature was used as training data for the Multilayer Perceptron (MLP) network. In this study, the training data was a dataset from NIST (National Institute of Standart and Technology), with 750 fingerprints consisting of male and female fingerprints, each as many as 375. The method could produce a total accuracy of 80.1%.

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Section: Literatures Reviewmentioning

confidence: 99%

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Suwarno

2023

SinkrOn

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“…In computer vison, CNN has been known as a powerful visual model as well as producing an accurate hierarchy of segmentation features. The model has also been known to make predictions relatively faster than other algorithms while maintaining competitive performance at the same time [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sgd, Adadelta, Adam Optimization in Gender Classification Using CNN

Mandasari¹,

Irfan²,

Wanayumini³

et al. 2023

JURTEKSI

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Gender classification is one of the most important tasks of video analysis. A machine learning-based approach was presented to identify male and female facial images with a data set of 2000 images taken from kaggles. This method plays a role in finding the weight value that gives the best output value. This study uses the most appropriate learning rate of each optimization method as a criterion for stopping training. The results showed that the Artificial Neural Network with Adam optimization produced the highest accuracy, which was 91.5% compared to the SGD and ADADELTA optimization methods. Deep Learning techniques that are applied extensively to image recognition used utilize Adam's optimizer method. Keywords: artificial neural networks; adadelta; adam; gender; sgm; Abstrak: Klasifikasi gender adalah salah satu tugas analisis video yang paling penting. Pendekatan berbasis machine learning disajikan untuk mengidentifikasi gambar wajah Pria dan Wanita dengan kumpulan data sebanyak 2000 gambar yang diambil dari kaggle. Metode ini berperan dalam menemukan nilai bobot yang memberikan nilai keluaran terbaik. Penelitian ini menggunakan learning rate yang paling sesuai dari masing-masing metode optimasi sebagai kriteria pemberhentian pelatihan. Hasil penelitian menunjukkan Jaringan Saraf Tiruan dengan optimasi Adam menghasilkan akurasi tertinggi yaitu 91,5 % dibandingkan dengan dengan metode optimasi SGD dan ADADELTA. Teknik Deep Learning yang diterapkan secara ekstensif pada pengenalan gambar yang digunakan memanfaatkan metode optimizer Adam. Kata kunci: Adadelta; Adam; Jaringan Syaraf; Gender; Tiruan; SGM;

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A Deep Dive into Gender Classification Using Inception V3: Performance and Insights

Shourie,

Anand,

Chauhan

et al. 2023

2023 Global Conference on Information Technologies and Communications (GCITC)

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Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image

Cited by 5 publications

References 12 publications

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Comparison of Sgd, Adadelta, Adam Optimization in Gender Classification Using CNN

A Deep Dive into Gender Classification Using Inception V3: Performance and Insights

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