Retracted: Gender Classification Based on Human Radiation Wave Analysis

Despite the fact that technology is progressing, people continue to consume deadly wild mushrooms due to their inability in classifying the different mushroom categories. Currently, no specific traits are defined to accurately predict the edibility of mushrooms based on their attributes. To overcome this challenge, Machine Learning [ML] can be used for identifying the poisonous mushroom based on the feature appearance. By considering the above, the Mushroom dataset extracted from UCI data warehouse are used for predicting the mushroom edibility level. The division of mushroom edible classes are achieved in four different ways. Firstly, the dataset is preprocessed with feature scaling and missing values. Secondly, raw data set is fitted to all the classifier with and without the presence of feature scaling. Thirdly, raw data is applied with the principal component analysis with 8, 10 and 12 components and PCA reduced dataset is fitted to all the classifier with and without the presence of feature scaling. Fourth, the raw data is applied with the Linear discriminant analysis and LDA reduced dataset is fitted to all the classifier with and without the presence of feature scaling. Fifth, raw data is applied with the Factor analysis with 8, 10 and 12 components and FA reduced dataset is fitted to all the classifier with and without the presence of feature scaling. Sixth, the performance of raw data set, PCA reduced data set, LDA reduced dataset and FA reduced dataset are compared by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done by using python language under Spyder platform with Anaconda Navigator. Experimental results show that, the kernel SVM, KNN and Adaboost classifier for the FA reduced dataset tends to retain the accuracy with 100% before and after feature scaling. The KNN classifier with LDA reduced dataset tends to retain the 96% accuracy before and after feature scaling. Kernel SVM, KNN and Adaboost classifier for the FA reduced dataset tends to retain the accuracy with 99% before and after feature scaling. From the above analysis, KNN classifier ismore efficient based on its accuracy with all PCA, LDA and FA reduced dataset.KeywordsMachine learningClassificationPrecisionAccuracyPCALDA and factor analysis

Gender classification based on facial features plays a central role in numerous intelligent applications such as surveillance cameras, interactive advertising, and human-computer interaction. This study aimed to compare two deep models, YOLOv9 and ResNet50, on face detection and gender classification, focusing on accuracy and inference speed. YOLOv9 performed well in terms of speed, with an inference time of 332 ms per image and a processing speed of 3 fps, and had a precision of 86.8%, 86.1% of recall, and 86.54% of F1-score. These performance characteristics make YOLOv9 suitable for real-time applications with high-speed response demands, even with moderately low classification accuracy. Conversely, ResNet50 was applied directly to gender classification after data preparation on images and had high classification accuracy, with a precision of 93.6%, 92% of recall, and 92.79% of F1-score. Its inference time was slower at 446.33 ms per image, with a 2.24 fps processing speed and a long training time of 9 hours and 18 minutes. These results show that YOLOv9 has high performance within a time scope of face detection, with reference to detecting enough faces within short timeframes with a limited number of computational resources, whereas ResNet50 has better classification accuracy. Depending on particular use case scenario demands, one corresponding model with a preferred feature can be selected: YOLOv9, if high-speed response is a concern during real-time applications, and ResNet50, if high classification accuracy is a concern.

In this paper, we propose a novel pattern to represent spatio-temporal information of gait appearance which is called Gait Principal Component Image (GPCI). GPCI is a grey-level image which compresses the spatiotemporal information by amplifying the dynamic variation of different body part. The detection of gait period is based on LLE coefficients and it is also a new attempt. KNN classifier is employed for gender classification. The framework can be applied in real-time setting because of its rapidity and robustness. The experimental results on IRIP Gait Database (32 males, 28 females) show that the proposed approach achieves a high accuracy in automatic gender classification.

Prior work by a subset of authors led to a development of optical camera platform, which has a capability of distinguishing between human and animal movement in an outdoor environment. Once the image is classified as a human, an idea of providing additional information and insights led to the exploration of gender (men vs women) and direction (left to right vs right to left) classifications. The proposed method classifies the human gender, based on the full body image oriented in a side view manner. An additional feature is to classify the direction of the movement. In the current paper, the Convolutional Neural Networks (CNNs) are used to distinguish between men and women gender classes and to identify the direction of the movement. Furthermore, different aspects of CNN are visualized (for example, attention heat maps, t-Distributed Stochastic Neighbor Embedding plot, etc.) to provide useful insights corresponding to classifications and misclassifications. Additionally, different CNN architectures were tried to figure out the best possible choice. The classification accuracies for gender (men vs women) and direction classification (left to right vs right to left) on the test data are close to 93.3% and 94%, respectively.

A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features of a scene and organizes the scenes into categories. Programmed techniques for correlating scenes with labels have a high potential for improving the performance of computer vision applications such as searching, retrieval and object recognition. The scene category system uses the feature vectors extracted from image data to classify test images. We have implemented a k-nearest neighbour (kNN) classifier which uses gray scale and Scale Invariant Feature Transform (SIFT) features as input and used support vector machine (SVM) classifier that uses GIST features as input. kNN classifier is used, as it has an advantage of working with a large amount of data and SVM is robust even when the training sample has some bias. The performance measure of the classifier was evaluated using confusion matrix and classification accuracy. We considered MIT-8 scene category, leaves, Caltech-20 and deer data sets for our experiments. The classification accuracy was found to be good. Results show that classification accuracy is increased significantly for classification using GIST features in SVM classifiers. We are exploring methods to obtain optimal performance of classifiers.

Gender classification is a crucial task in most forensic cases. In most cases, skeleton remains are employed and there are different parts of human skeleton available for the classification process. Every part of skeleton contains different types of features which benefits toward gender classification. However, some features cannot contribute toward classification as features carry no information on gender. Hence, this article proposed a particle swarm optimisation-based (PSO) feature selection and optimised BPNN model as a gender classification framework. Initially, PSO selects the most significant features that lead to an accurate classification process. In the BPNN process, the parameter tuning based on cross-validation technique is applied where the model is able to find a good combination of learning rate and momentum. This article utilised data from Goldman Osteometric dataset, Clavicle collection, and George Murray Black collection. The result shows that the accuracy of gender classification is improved for every dataset via the proposed framework.

Gender classification from facial images plays a significant role in biometric technology viz. gender medicine, surveillance, electronic banking system and human computer interaction. However, it has many challenges due to variations of pose, expression, aging, race, make-up, occlusion and illumination. In the proposed system, spatially enhanced local binary pattern (SLBP) and histogram of oriented gradients (HOG) are extracted to classify the human gender with SVM classifier. This hybrid feature selection has increased the power of the proposed system due to its representation of texture micro-patterns and local shape by capturing the edge or gradient structure form the image. The gender classification accuracy is studied by using the local feature representation of the face images separately and also these features are concatenated to provide a better recognition rate. The combination of two different local descriptors provides good representation of face image and this is given to SVM classifier which classifies as male or female. Also, the proposed work is compared with other two traditional classifiers such as k-nearest neighbor and sparse representation classifier. The performance was evaluated on FERET and LFW database. The highest classification accuracy 99.1% is achieved on FERET database and 95.7% is achieved on LFW database by applying cubic SVM with fusion of SLBP and HOG features.

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.

Gender Detection and Classification from Fingerprints Using Pixel Count

: Gender is the most critical demographic attribute of human beings. Gender classification from facial images is widely used in computer vision for surveillance, a straightforward task for humans, and a challenging task for machines. This paper provides a study of facial gender classification in the field of computer vision. We highlight the challenges faced during the image capturing process due to factors such as illumination, angle, occlusion, and expressions. We also review various feature extraction approaches used by previous researchers to perform the facial gender classification task. This paper also compares the performances of previous methods on various face datasets to perform gender classification. We observed from the previous studies that good performance had been achieved for a dataset of facial images taken in a controlled environment. However, much more work needs to be done to increase the accuracy and robustness of facial gender classification, especially in uncontrolled environments.

Gender classification is an important task in video surveillance. Terrorists and other criminals mostly act from a distance. Therefore, it is proposed that a gait-based method that recognizes humans remotely is required. In this paper, we propose a new method of gait-based gender classification based on the Kinect sensor, using a model based on the feature set, 'Dynamic Distance Feature (DDF)'. Nearest Neighbour (NN), Linear Discriminant Classifier (LDC), and Support Vector Machine (SVM) are used separately as a classification method. The method is tested based on skeletal data provided by the Microsoft Kinect. The experimental results show that the proposed method provided significant results by achieving 96.67%, 91% and 90% accuracy for gender classification using NN, LDC, and SVM respectively.

Gender classification from human face images has attracted researchers over the past decade. It has great impact in different fields including defense, human-computer interaction, surveillance industry, and mobile applications. Many methods and techniques have been proposed depending on clear digital images and complex feature extraction preprocessing. However, most recent critical real systems use thermal cameras. This paper has the novelty of utilizing thermal images in gender classification. It proposes a unique approach called IRT_ResNet that adopts residual network (ResNet) model with different layer configurations: 18, 50, and 101. Two different datasets of thermal images have been leveraged to train and test these models. The proposed approach has been compared with convolutional neural network (CNN), principal component analysis (PCA), local binary pattern (LBP), and scale invariant feature transform (SIFT). The experimental results show that the proposed model has higher overall classification accuracy, precision, and F-score compared to the other techniques.

The classification or recognition of human voices are required for different applications such as speech emotion recognition, medicals, communications software, and security. However, gender classification of speech signals is complex aspect of speech recognition systems; thereby requiring robust signal processing strategies. This paper investigates the efficacy of Artificial Intelligence (AI) for gender classification in speech signals. Two different AI-based gender voice classifiers namely K-Nearest Neighbor (K-NN) and Long Short-Term Memory (LSTM) were developed. First, speech signals were recorded from different male and female speakers at a sampling rate of 48 kHz. Each of the raw speech signals was filtered and the useful portion of the signal was segmented. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrum (MS) features were extracted from each signal via framing, hamming window, and FFT. An equal number of observations each for the male and female classes were generated. The total 2000 observations were partitioned into 80% for training and 20% for testing. The training dataset was used to train both the K-NN and LSTM classifiers. The results obtained from testing with the testing dataset showed that the K-NN classifier gave precision, recall, accuracy and F1-score values of 0.9852, 0.9850, 0.9925 and 0.9851, respectively, whereas the LSTM classifier gave 0.9132, 0.9091, 0.9525, and 0.9091, respectively. The classifiers achieve more than 0.95 (or 95%) classification accuracy; thereby demonstrating the efficacy of the AI strategies in distinguishing between a male voice and a female voice.

MEG: Texture operators for multi-expert gender classification

In this paper we explore the robustness of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the Civilian American and European Surface Anthropometry Resource Project (CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International). This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of high density point cloud found in the CAESAR database. In our previous has shown that when point cloud densities are reduced to levels that might be obtained using stand-off sensors; gender classification accuracy degrades. In this paper we show the results of how the classification accuracy degrades as a function of center of mass displacements.