Face Gender Classification Research Articles

Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes

This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA+LDA and SVM) and two performance measures (CCR and d′) is provided over single- and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs.

Three robust features extraction approaches for facial gender classification

This research paper introduces three robust approaches for features extraction for gender classification. The first approach is based on using Discrete Cosine Transform (DCT) and consists of two different methods for calculating features values. The second approach is based on the extraction of texture features using the gray-level cooccurrence matrix (GLCM). The third approach is based on 2D-wavelet transform. The extracted features vectors are classified using SVM. For precise evaluation, the databases used for gender evaluation are based on images from the AT@T, Faces94, UMIST, and color FERET databases. K-fold cross validation is used in training the SVM. The accuracies of gender classification when using one of the two proposed DCT methods for features extraction are 98.6 %, 99.97 %, 99.90 %, and 93.3 % with 2-fold cross validation, and 98.93 %, 100 %, 99.9 %, and 92.18 % with 5-fold cross validation. The accuracies of GLCM texture features approach for facial gender classification are 98.8 %, 99.6 %, 100 %, and 93.11 %, for AT@T, Faces94, UMIST, and FERET, databases. The accuracies for all databases when using 2D-WT are ranging between 96.18 % and 99.6 % except FERET and its accuracy is 92 %.

Viewpoint invariant gender recognition

In this paper, we address a problem of gender classification of faces taken from arbitrary viewpoints. We use a face model for accurate face localisation based on a combination of appearance and geometry. A probabilistic matching of particular traits on face allows to classify the gender of face even when pose changes. We deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. Such feature contributes to determine the gender associated to a given face. We evaluate the model by testing it simultaneously in face localisation and gender classification experiments on PIE, FERET and CMU-profiles databases. The experimental results show that the probabilistic invariant model performs well to detect face and gives a rate of 92.1% of accurate gender classification in the presence of viewpoint changes and large appearance variability of faces.

3D Facial Gender Classification Based on Multi-angle LBP Feature

Facial gender classification is a challenging topic,and it s still not perfect until now.In this paper,we propose a series of methods of gender classification based on three-dimension faces.Automatic front-pose adjustment is needed through local region iterative closest point(ICP) registration firstly;then we do pitching rotating and extract multi-angle LBP features from depth thumbnail map in di?erent viewing angles;at last,we use support vector machine(SVM) classifier to do training and prediction.This algorithm has been experimented on CASIA database,and for the neutral faces in this database,we can get a highest correct classification rate of 98.374%.

Periocular Gender Classification using Global ICA Features for Poor Quality Images

In recent years, the research over emerging trends of biometric has grabbed a lot of attention. Periocular biometric is one such field. Researchers have made attempts to extract computationally intensive local features from high quality periocular images. In contrast, this paper proposes a novel approach of extracting global features from periocular region of poor quality grayscale images for gender classification. Global gender features are extracted using independent component analysis and are evaluated using conventional neural network techniques, and further their performance is compared. All relevant experiments are held on periocular region cropped from FERET face database. The results exhibit promising classification accuracy establishing the fact that the approach can work in fusion with existing facial gender classification systems to help in improving its accuracy.

Facial Gender Classification with Eigenfaces and Least Squares Support Vector Machine*

Facial Gender Classification with Eigenfaces and Least Squares Support Vector Machine*

Facial Gender Classification with Eigenfaces and Least Squares Support Vector Machine

Facial Gender Classification with Eigenfaces and Least Squares Support Vector Machine