Andere Kunden interessierten sich auch für
![Satellite Image Classification Using K-Means and SVD Techniques]( "Satellite Image Classification Using K-Means and SVD Techniques")
Satellite Image Classification Using K-Means and SVD Techniques32,99 €![Denoising of Digital Images using Hybrid Computing]( "Denoising of Digital Images using Hybrid Computing")
Denoising of Digital Images using Hybrid Computing45,99 €![Restoration of Noisy Blurred Images Using FFT and DWT]( "Restoration of Noisy Blurred Images Using FFT and DWT")
Restoration of Noisy Blurred Images Using FFT and DWT46,99 €![Contrast Enhancement of Cancer Cell Images Using Fuzzy Logic]( "Contrast Enhancement of Cancer Cell Images Using Fuzzy Logic")
Contrast Enhancement of Cancer Cell Images Using Fuzzy Logic26,99 €![Three Dimensional Facial Modeling and Animation]( "Three Dimensional Facial Modeling and Animation")
Three Dimensional Facial Modeling and Animation32,99 €![Automated Modified Region Growing Technique for Ultrasound Images]( "Automated Modified Region Growing Technique for Ultrasound Images")
Automated Modified Region Growing Technique for Ultrasound Images36,99 €![Automated Biomarker Extraction from Medical Images]( "Automated Biomarker Extraction from Medical Images")
Automated Biomarker Extraction from Medical Images38,99 €
The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.