Gender Classification using Geometric Facial Features (original) (raw)

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 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.

Critical Evaluation of Frontal Image-Based Gender Classification Techniques

The face describes the personality of humans and has adequate importance in the identification and verification process. The human face provides, information as age, gender, face expression and ethnicity. Research has been carried out in the area of face detection, identification, verification, and gender classification to correctly identify humans. The focus of this paper is on gender classification, for which various methods have been formulated based on the measurements of face features. An efficient technique of gender classification helps in accurate identification of a person as male or female and also enhances the performance of other applications like Computer-User Interface, Investigation, Monitoring, Business Profiling and Human Computer Interaction (HCI). In this paper, the most prominent gender classification techniques have been evaluated in terms of their strengths and limitations.

Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the best possible classification accuracy.

Back Propagation Neural Network Based Gender Classification Technique Based on Facial Features

2013

The gender recognition system with large sets of training sets for personal identification normally attains good accuracy. The features set is applied to three different applications: Preprocessing, Feature Extraction and Classification. The gender are classified on the basis of distance between eyebrow to eye, eyebrow to nose top, nose top to mouth, eye to mouth, left eye to right eye, width of nose, width of mouth. First to extract these features by using Viola Jones algorithm and then apply Artificial Neural Network. The features set is applied to three different applications: face recognition, facial expressions recognition and gender classification. In this paper described two phases such as feature extraction phase and classification phase. The proposed system produced very promising recognition rates for our applications with same set of features and classifiers.

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.

RECOGNIZING GENDER THROUGH FACIAL IMAGE USING SUPPORT VECTOR MACHINE

The face is one part of the human body that has special characteristics, which is often used to distinguish the identity of one individual and another. Facial recognition is very important to be developed since this application is applied in the security system. The recognition of sex is one part of the face recognition. Gender plays an important role in our interactions in the community and with the computer. Classification gender of the face image can be applied in the field of demographic data collection, human-computer interface (customize the behavior of software in connection with the sex of the user) and others. The purpose of this study is to make implementation of the system in recognizing the gender on facial image or filling the form with the Gender Recognition face image that is able to recognize a person's sex quickly and accurately, and run well. This study used methods of Two Dimensional Linear Discriminant Analysis (TDLDA) for feature extraction, which directly assess within-class scatter matrix of the transformation matrix without any image into a vector image, and this resolves the singular problem within-class scatter matrix. To obtain optimal recognition results of the classification method, it used the classification Support Vector Machine. This study integrates TDLDA and SVM methods for the introduction of gender based on facial image. The combination of both methods proves the optimal results with an accuracy of 74% to 92% with a test that uses a database of faces taken from http://www.advancedsourcecode.com.

Three robust features extraction approaches for facial gender classification

The Visual Computer, 2013

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. Kfold 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 %.

Gender classification using image processing techniques: A survey

2011

Classification has emerged as a leading technique for problem solution and optimization. Classification has been used extensively in several problems domains. Automated gender classification is an area of great significance and has great potential for future research. It offers several industrial applications in near future such as monitoring, surveillance, commercial profiling and human computer interaction. Different methods have been proposed for gender classification like gait, iris and hand shape. However, majority of techniques for gender classification are based on facial information. In this paper, a comparative study of gender classification using different techniques is presented. The major emphasis of this work is on the critical evaluation of different techniques used for gender classification. The comparative evaluation has highlighted major strengths and limitations of existing gender classification techniques. Taking an overview of these major problems, our research is focused on summarizing the literature by highlighting its strengths and limitations. This study also presents several areas of future research in the domain of gender classification.