Effects of the facial and racial features on gender classification (original) (raw)

Gender classification using face images: a review

INTERNATIONAL JOURNAL OF LATEST TRENDS IN ENGINEERING AND TECHNOLOGY

In field of Image processing face is one of the most important biometric traits and is becoming more popular for the security purpose in now a days. During past several years classification of gender from facial images has gained enormous significance and has become a popular area of research. Many researches have done on the gender classification from several years. Still, this is a very important field of image processing because of its applications in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have high importance in this area. Gender classification using facial and racial features can be used as part of a face recognition process. This paper comparison of different gender classification techniques and use of different racial features such as eyes, nose, mouth etc. for gender classification.

Gender Classification of Human Faces Using Class Based PCA

Gender classification is a binary classification system where system has to assign a given test image to one of the two classes (male or female). The gender classification system with large set of training data normally gives good accuracy. But to achieve good accuracy with small training data is a difficult task. This paper proposes an algorithm for gender classification with small training data and it gives good accuracy even with one image per person for training. The system contains mainly two parts: feature vector generation and classification. Feature vector generation is done with PCA (Principal Component Analysis ).

Face Recognition and Gender Classification using Face Images

In Image processing the face easily approachable biometric trait. And it is still a challenging task for face recognition and classification of gender, ethnicity, age etc. To reduce search time of identification of face image as male or female the face recognition and gender classification is most important. In this project minimum distance classifier is used with Principal Component Analysis based gender classification. FEI Face Database of 100 images (50 male and 50 female face images) is used for the face recognition and gender classification. It is observed that face recognition accuracy of project is 85% and gender classification accuracy is 98%.

A Gender Recognition System Using Facial Images with High Dimensional Data

Malaysian Journal of Applied Sciences, 2021

Gender recognition has been seen as an interesting research area that plays important roles in many fields of study. Studies from MIT and Microsoft clearly showed that the female gender was poorly recognized especially among dark-skinned nationals. The focus of this paper is to present a technique that categorise gender among dark-skinned people. The classification was done using SVM on sets of images gathered locally and publicly. Analysis includes; face detection using Viola-Jones algorithm, extraction of Histogram of Oriented Gradient and Rotation Invariant LBP (RILBP) features and trained with SVM classifier. PCA was performed on both the HOG and RILBP descriptors to extract high dimensional features. Various success rates were recorded, however, PCA on RILBP performed best with an accuracy of 99.6% and 99.8% respectively on the public and local datasets. This system will be of immense benefit in application areas like social interaction and targeted advertisement.

Classification of Face Images Based on Gender using Dimensionality Reduction Techniques and SVM

2007

This report presents gender classification based on facial images using dimensionality reduction techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) along with Support Vector Machine (SVM). The input dataset is divided into training and testing dataset and experiments are performed by varying dataset size. The effect of performing image intensity normalization, histogram equalization, and input scaling are observed. The outcomes of the experiments are analogous to published works that apply similar techniques.

Identification of Gender from Facial Features

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Increasing population and changing lifestyle become a more confusing task for detecting gender from facial images. To solve such a fragile problem several handy approaches are readily available in computer vision. Although, very few of these approaches achieve good accuracy. The features like lightning, illumination, noise, ethnicity, and various facial expression hamper the correctness of the images. Keeping these things in mind, we propose our research work on the identification of gender from facial features. The major component of face recognition is to develop a machine learning model which will classify the images this can be done by haar-cascade-classifier. To train the model with images more accurately we would perform few image processing concepts for the data to perform data analysis and preprocessing for structuring our data. This can be done by OpenCV. After that, we have used PCA ( Principle Comprehend Analysis ) to compute Eigenvalues and for the optimal components, we will get the class name from the knowledge base and confidence score from the SVM-based face recognition model. In our project work, we get good accuracy.

Gender Recognition Using Facial Images

In this study, gender classification is performed based on front faç ade photos of 100 male and 100 female. In order to demonstrate the internal face images are aligned and cropped.. Even though some images are cropped about ears and hairs with the expense of the information loss about gender information at those parts, the main aim is achieving gender classification on internal face of the human body. It has been generated that 7 x 200 matrix which obtained from images that include 3 statistical values (average, standard deviation and entropy) and 4 parameters of GLCM (Gray Level Co-occurrence Matrix). 60% gender classification accuracy rate is achieved based on the generated frontal face image data set. As a secondary method, features are extracted by means of GLCM method, followed by application of 2D DWT (The Discrete wavelet transform) technique on the original images. it has been established attribute of original images by respectively DWT (The Discrete wavelet transform) and GLCM (Gray Level Co-occurrence Matrix). When first method is used for 7 photos (7 attitude) which are output 2D DWT, set volume is 49 x 200. Used to 5 different wavelets of relatives and the highest achievement is found at Coiflets Wavelets Filter by 88%. Second method increases to first method's achievement by %46.

Gender Identification Using Face Images

2018

Gender classification is arguably one of the more important visual tasks for an extremely social animal like us humans— many social interactions critically depend on the correct gender perception of the parties involved. Many research studies have been conducted on face recognition to improve its accuracy since the first research. But face recognition still far from achieving accuracy that on par with human facial features and gender classification. However, all of them have still disadvantage such as not complete reflection about face structure or face texture. In general, gender classification in supervised learning setting requires extraction of features from face images, training classifiers using those features (eyes, nose and mouth) and finally performing classification of new faces. This work uses appearance-based approach with dimensionality reduction techniques for feature extraction. The features extracted from the training set are used for training KNN/SVM classifier. And...

A Study on Gender Identification from Frontal Facial Images

2015

Gender classification/identification is very interesting topic which not only for its wide range applications (security, education, demographics study etc.) but also its ability ofboosting performance of many other applications such as face recognition, age classification, human computer interface etc. This project describe two traditional global method, the Eigenface [8] and the Fisherface [9] and one popular local method, Local Binary Pattern [10] in gender classification problem. The feature selection, learning algorithm, and classification are presented. The experiment results are evaluated with wellknown gender aging dataset - FG-NET