Gender Identification System Research Articles

Real-time Face-based Gender Identification System Using Pelican Support Vector Machine

Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data.

Periocular Region based Gender Identification using Transfer Learning

COVID-19 broke out at the end of 2019 and is still affecting the lifestyle of the people. To protect ourselves from the deadly disease, wearing a face mask is recommended when coming in contact with others. The usage of face masks in our daily lives leads to the problem of occlusion for facial image-based gender identification systems. Gender Identification System is an application of computer vision used in biometrics, consumer identification, and security systems. In the situation of masked faces, the only visible part of the face is the area around the eye, i.e., Periocular Region. The motivation behind this research is to build a gender identification system from periocular images with pre-trained CNN models using the Transfer Learning approach. In the proposed methodology, the optimal periocular ROI is first extracted and passed to the different pre-trained CNN models (VGG16, VGG19, ResNet50, ResNet101, Inception V3, DenseNet121) for feature extraction. Then, the fully connected layers are added to the base models for classification. The proposed approach with VGG19, ResNet101, and ResNet50 as the base models outperform existing models with an average accuracy of 98.65%, 98.96%, and 98.99%, respectively, in different experiments on the benchmark UBIPr dataset.

Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders.

Classification of Gender Individual Identification Using Local Binary Pattern on Palatine Rugae Image

Major disasters caused many casualties with the condition of the damaged bodies. It causes the individual identification process to be ineffective through biometric characteristics (such as lips and fingerprints). However, the palatine rugae can carry the individual identification process. Palatine rugae have unique and individual characteristics and are more resistant to trauma because of their internal location. In this study, an individual identification system is proposed to identify gender using the image of palatine rugae. The proposed system is developed by several algorithms and methods, such as Local Binary Pattern (LBP) as the feature extraction method and K-Nearest Neighbor (KNN) as the classification method. Based on the result of the system performed test, the proposed system can identify the gender of an individual by the combination of recognized palatine rugae patterns. The system achieved an accuracy test result of 100% with a specific configuration of LBP and KNN. The research contribution in this study is to develop the individual gender identification system, which proceeds with the palatine rugae pattern image with unique biometric characteristics as an input. The system applied several methods and algorithms, such as Geometric Active Contour (GAC) as a segmentation algorithm, Local Binary Pattern (LBP) as a feature extraction method, and K Nearest Neighbor (KNN) as a classification method.

Identifikasi Jenis Kelamin Secara Real Time Berdasarkan Suara Pada Raspberry Pi

The voice of each speaker has a unique specific character, influenced by gender, age, emotion, dialect, etc. The use of voice-based gender identification is growing rapidly, such as in the fields of security systems, speech recognition, artificial intelligence, etc. However, in speech processing, there are difficulties where the characteristics of the speech signal based on increasing age are difficult to determine accuracy, and there are overlapping fundamental frequency values between males and females. In this research, modeling of a gender identification system based on voice in real-time has been carried out on a Raspberry Pi device. This system is implemented by 2 methods, namely the YIN algorithm and feature extraction of Mel-Frequency Cepstral Coefficient (MFCC). The test results showed that the success of identification in the tuning parameters of scheme two is better than the first scheme by narrowing the overlapping frequency parameters. In the female test data in the closed test, the accuracy is from 98% to 100%, then in the open test starts from 92% to 96%. Meanwhile, the test data for the male closed test increased from 92% to 98%, and the open test started at 90% and rose to 94%. It indicates that the data used in this research is more suitable to use the second scheme parameter tuning to increase the accuracy of the results.

An attention based multi-modal gender identification system for social media users

An attention based multi-modal gender identification system for social media users

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.

Human gender revelation based on facial features and shape and structure of finger nails

The research on face recognition has become an integral part in the field of many engineering areas. The variation in the appearance of an human image makes proper face recognition a difficult task. However, face identification is an extremely important aspect of human identification system especially in gender identification system. Thus, this paper has aimed to detect the gender of human beings based on different frontal facial features. In this case we have considered facial images with different emotions like neutral, happy, sad, angry and surprised, respectively. To perform gender identification, frontal facial features are detected and extracted based on Region of Interest principle. Then, Fast Fourier Transform and Discrete Cosine Transform algorithmic logics have been incorporated to transform the input data from spatial domain to frequency domain, and on the resultant data further operations are performed to accomplish the task of gender identification. This has made the proffered algorithm different and unique from the existing algorithms. In addition, an alternate approach has also been proposed for gender identification and revelation based on the shape and structure of human finger nails. The latter method has primarily emphasized on the structural organization of finger nails in both male and female. The propounded algorithm has also succeeded in determining the gender of human being based on the shape and structure of human finger nails.

Facial gender recognition using Gabor-DCT feature extraction

Facial Gender Identification has vast application in human computer interaction, determines customer profile in shopping centers, and restricted permission to enter in prohibited zone, criminal profile analysis. This paper presents a robust process for illumination invariant compact feature extraction using Gabor filter for the automatic recognition for facial gender identification system. Face has uniqueness in edges and texture pattern for different gender category. Gabor filter can extract edge and textural patterns of faces but generate problem of huge dimensions and redundancy feature coefficients. In order to enhance the efficiency and accuracy of the system, this problem of enormous redundancy as well as dimension can be solved by proposing a new feature namely average-DCT feature reduction technique. Proposed Gabor-DCT has precise, accurate and compact feature pattern as well as early throughput for facial gender identification compared to other state of art method of Gabor filter.

Development of Human Age and Gender Identification System from Teeth, Wrist and Femur Images

Human age determination and gender identification forms a major step and it is one of the keys in the gender and age estimation process in the forensic identification process. Machine Learning and Biomedical Image Processing Techniques have enormously contributed in the field of Medical Image Analysis, classification and recognition. Computers have been widely used in the field of medical research over the past few decades. But fewer contributions have been made in the area of forensics by researchers. There is a scope for researches, where they can make their contributions especially in medical image analysis that too in the age and gender identification using digital radio-graphs. Teeth are frequently better preserved than other materials of human body. Wrist bone has more unique features than any other bone features of human. Long bone plays vital role in age estimation and gender identification. Usually long bones of male tends to be bit longer and bulk in size compared to female bones. Due to the special features of teeth, wrist and femur, they have been considered as input for our research experiments. In this paper, we have identified the features from teeth, wrist and femur bone which will critically help in the identification of age and gender of a human.

Automated gait-based gender identification using fuzzy local binary patterns with tuned parameters

With the rapid advances in sensor networks and internet-of-things, several applications have evolved to ubiquitously monitor what is happening in smart environments. This paper designs a gait-based gender identification system based on an improved method of fuzzy local binary patterns for texture analysis. The moving person’s silhouette is extracted and a texture image is constructed to summarize structural and dynamical variations over one gait cycle. Then, histograms representing soft pattern variations are constructed and used as feature vectors. The proposed method employs grid search to find the optimal hyper-parameters for the feature extraction method. Finally, support vector machines are trained to predict the gender of the walking person. The performance is evaluated and compared with four other local binary pattern (LBP) related texture descriptors. The results show significant improvements in the performance under different walking conditions.

GENDER IDENTIFICATION SYSTEM FOR CRIME SCENCE ANALYSIS USING FINGERPRINTS

Gender identification or classification is a challenging task in computer vision as the biometrics of male and female such as fingerprints, face, vein have many variations. Among the various biometrics, fingerprints are commonly available in a crime scene. In this, study, gender identification system for crime scene analysis using fingerprints is presented. Initially, the fingerprints are de-noised by median filter and Otsu thresholding is employed to binarize the fingerprints in the preprocessing stage. Then, the features are extracted by Box-Cox transformation method. Finally, the classification is made by logistic regression classifier. A better classification accuracy of 96% is achieved by the gender identification system using Box-Cox transformation and logistic regression classifier.

Speech Synthesis for Gender Classification

<p class="0abstract">This paper presents a gender identification system to be used for call forwarding in health related communications. The system listens to the caller then using speech synthesis, image processing, and linear support vector machine SVM identifies either he or she is a male or a female. This solution is imperative in a conservative country such as the Kingdom of Saudi Arabia in order to forward the call to a male or female practitioner. The originality of the approach is that no transcription is used to learn SVM models. To identify the gender of the caller, the trained SVM model of the reference pieces are compared to transcripts of the audio frequency record and are using the Levenshtein distance. For the identification of gender, we obtain an accuracy rate of 94% on a test flow containing 449 pieces of speech clips.</p>

Costumes as Sexual Symbols: Archeological Evidences from the Small River Cemetery (Xinjiang, China)

Based on the costumes unearthed from Small River Cemetery (Xinjiang, China), this article discussed the certain dressing customs of males and females in the Bronze Age Xinjiang. According to the analysis of the felt caps, loincloths, woolen cloaks and other burial goods, the paper re-pictured the gender identification system of the dressing culture of the ancient people from Small River Cemetery.

Wavelet Packet and Percent of Energy Distribution with Neural Networks Based Gender Identification System

Wavelet Packet and Percent of Energy Distribution with Neural Networks Based Gender Identification System

Glottal Excitation Feature based Gender Identification System using Ergodic HMM

In this paper, through different experimental studies it is demonstrated that the time varying glottal excitation component of speech can be exploited for text independent gender recognition studies. Linear prediction (LP) residual is used as a representation of excitation information in speech. The gender-specific information in the excitation of voiced speech is captured using the Hidden Markov Models (HMMs). The decrease in the error during training and recognizing genders during testing phase close to 100 % accuracy demonstrates that the excitation component of speech contains gender-specific information and is indeed being effectively captured by continuous Ergodic HMM. A gender recognition study using gender specific features for different HMM states, mixture components, size of testing data on the performance of the gender recognition is evaluated. We demonstrate the gender recognition studies on TIMIT database. KeywordsGender, Hidden Markov Model (HMM); LPC; MFCC

Pitch‐based gender identification with two‐stage classification

AbstractIn this paper, we address the speech‐based gender identification problem. Mel‐Frequency Cepstral Coefficients (MFCC) of voice samples are typically used as the features for gender identification. However, MFCC‐based classification incurs high complexity. This paper proposes a novel pitch‐based gender identification system with a two‐stage classifier to ensure accurate identification and low complexity. The first stage of the classifier identifies and labels all the speakers whose pitch clearly indicates the gender of the speaker; the complexity of this stage is very low since only threshold‐based decision rule on a scalar (i.e., pitch) is used. The ambiguous voice samples from all the other speakers (which cannot be classified with high accuracy by the first stage, and can be regarded as suspicious speakers or difficult cases) are forwarded to the second‐stage for finer examination; the second‐stage of our classifier uses Gaussian Mixture Model to accurately isolate voice samples based on gender. Experiment results show that our system is speech language/content independent, microphone independent, and robust against noisy recording conditions. Our system is extremely accurate with probability of correct classification of 98.65%, and very efficient with about 5 s required for feature extraction and classification. Copyright © 2011 John Wiley & Sons, Ltd.

A Gender Identification System for Customers in a Shop Using Infrared Area Scanners

Information about customers in shops plays an important role in marketing analysis. Currently, in convenience stores and supermarkets, the identification of customer's gender is examined by clerks. On the other hand, gender identification systems using camera images are investigated. However, these systems have a problem of invading human privacies in identifying attributes of customers. The proposed system identifies gender by using infrared area scanners and Bayesian network. In the proposed system, since infrared area scanners do not take customers' images directly, invasion of privacies are not occurred. The proposed method uses three parameters of height, walking speed and pace for humans. In general, it is shown that these parameters have factors of sexual distinction in humans, and Bayesian network is designed with these three parameters. The proposed method resolves the existent problems of restricting the locations where the systems are set and invading human privacies. Experimental results using data obtained from 450 people show that the identification rate for the proposed method was 91.3% on the average of both of male and female identifications.

A Gender Identification System Using Infrared Area Scanners

A Gender Identification System Using Infrared Area Scanners

Gender Identification System from Facial Image Using Artificial Neural Network

This paper presents the implementation of gender identification system from a front view facial image using artificial neural network. In this system the facial images from different persons were taken as its input. At first, face images were projected onto a feature space that span the significant variations among face images. The features of the images were extracted using a compression technique that used the artificial neural network for compression. The extracted feature was then given to the input of the multi-layer feed forward neural network. Thus the network was trained and created a knowledge base. Finally, when a facial image was given for identification, the recognition part of the identification system identified the gender with the help of previously stored knowledge base.