Gender classification based on fuzzy clustering and principal component analysis

Abstract

Gender classification is one of the most challenging problems in computer vision. Facial gender detection of neonates and children is also known as a highly demanding issue for human observers. This study proposes a novel gender classification method using frontal facial images of people. The proposed approach employs principal component analysis (PCA) and fuzzy clustering technique, respectively, for feature extraction and classification steps. In other words, PCA is applied to extract the most appropriate features from images as well as reducing the dimensionality of data. The extracted features are then used to assign the new images to appropriate classes – male or female – based on fuzzy clustering. The computational time and accuracy of the proposed method are examined together and the prominence of the proposed approach compared to most of the other well‐known competing methods is proved, especially for younger faces. Experimental results indicate the considerable classification accuracies which have been acquired for FG‐Net, Stanford and FERET databases. Meanwhile, since the proposed algorithm is relatively straightforward, its computational time is reasonable and often less than the other state‐of‐the‐art gender classification methods.

Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization Methods

“…The study was investigated using non-linear Support Vector Machine (SVM) classifier, and latter SVM along with dimension reduction techniques such as Principal Component Analysis (PCA) and Locally Linear Embedding (LLE) to demonstrate the superiority of dimension reduction techniques [9,12]. Detecting and aligning the face images automatically have been studied in the previous work of gender classification [21].…”

Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm

“…The category consistent with the forecasted day needs to be identified after sample classification. The Euclidean distance is calculated between the predicted day and the above categories one by one [26]:…”

“…Fuzzy clustering (FC) is a mathematical technique that classifies objects according to their characteristics [26]. In view of the fact that the daily load curves with similar influential factors of charging stations are basically consistent, good prediction results can be achieved by the use of samples on similar days.…”

Palmprint gender classification by convolutional neural network

“…In the first category, there are many approaches for gender classification. Hassanpour et al [28] applied PCA and the fuzzy clustering technique for facial gender classification, which achieved 91.89, 95.4 and 90.9% accuracy rates for the FG-Net, Stanford and FERET databases, respectively. Garg and Trivedi [10] combined Viola&Jones face detection [29], topographic independent component analysis feature extraction, and the SVM classifier for face gender classification.…”