Gender classification based on fuzzy clustering and principal component analysis

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

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.

K-NN is a classification algorithm which suitable for large amounts of data and have higher accuracy for internet traffic classification, unfortunately K-NN algorithm has disadvantage in computation time because K-NN algorithm calculates the distance of all data in some dataset. This research provide alternative solution to overcome K-NN computation time, the alternative solution is to implement clustering process before the classification process. Clustering process does not require high computation time. Fuzzy C-Mean algorithm is implemented in this research. The Fuzzy C-Mean algorithm clusters the based datasets that be entered. Fuzzy C-Mean has disadvantage of clustering, that is the results are often not the same even though the input data are same, and the initial dataset that of the Fuzzy C-Mean is not optimal, to optimize the initial datasets, in this research, feature selection algorithm is used, after selecting the main feature of dataset, the output from fuzzy C-Mean become consistent. Selection of the features is a method that is expected to provide an initial dataset that is optimum for the algorithm Fuzzy C-Means. Algorithms for feature selection in this study used is Principal Component Analysis (PCA). PCA reduced nonsignificant attribute to created optimal dataset and can improve performance clustering and classification algorithm. Results of this research is clustering and principal feature selection give signifanct impact in accuracy and computation time for internet traffic classification. The combination from this three methods have successfully modeled to generate a data classification method of internet bandwidth usage.

Gender classification is one of the challenging problems in computer vision. Many interactive applications need to exactly recognize human genders. In this paper, we are carrying out some experiments to classify the human gender in conditions of low captured video resolution. We use Local Binary Pattern, Gray Level Co-occurrence Matrix to extract the features from faces and Gait Energy Motion, Gait Energy Image for gaits. We propose to combine face and gait features with the combination classifier to enhance gender classification performance.KeywordsGender ClassificationFacesGaitsLocal binary patternGait Energy MotionGait Energy Image

Gender classification is challenging. Methods for gender classification need to discriminate subtle differences between male and female. Bag-of-Features (BoF) method with sparse coding has been proven very powerful in image classification. In this paper, we apply BoF method for gender classification. We use two sets of images: training images and testing images. All images are represented by a set of Scale-Invariant Feature Transform (SIFT) descriptors. In training stage, using sparse coding, Visual Words Dictionary (VWD) is constructed from SIFT descriptors extracted from training images. In testing, SIFT descriptors of testing images are approximated by visual words in VWD. The choices of approximating visual words determine the classification decision. We apply our method and another two popular methods on public dataset for gender classification. We achieved promising results.

As one of the most important soft biometrics, gender has substantial applications in various areas such as demography and human-computer interaction. Successful gender estimation of face images taken under real-world also contributes to improving the face identification results in the wild. However, most existing gender classification methods estimate gender under well controlled environment, which limits its implementation in real-world applications. In this paper, we propose a new network architecture to combine the coarse appearance features with delicate facial features for gender estimation task. We call this method “coarse and fine” to give a harsh description of the gender estimation process. Trained on the large scale uncontrolled CelebA dataset without any alignment, the proposed network tries to learn how to estimate gender of real-world face images. Cross-database experiments on LFWA and CASIA-WebFace dataset show the superiority of our proposed method.

This paper presents an approach to digit recognition using single layer neural network classifier with Principal Component Analysis (PCA). The handwritten digit recognition is an important area of research as there are so many applications which are using handwritten recognition and it can also be applied to new application. There are many algorithms applied to this computer vision problem and many more algorithms are continuously developed on this to make the handwritten recognition classify digits more accurately with less computation involved. The proposed model in this paper aims to reduce the features to reduce computation requirements and successfully classify the digit into 10 categories (0 to 9). The system designed consists of backward propagation (BP) neural network and is trained and tested on the MNIST dataset of handwritten digit. The proposed system was able to obtain 98.39% accuracy on the MNIST 10,000 test dataset. The Principal Component Analysis (PCA) is used for feature extraction to curtail the computational and training time and at the same time produce high accuracy. It was clearly observed that the training time is reduced by up to 80% depending on the number of principal component selected. We will consider not only the accuracy, but also the training time, recognition time and memory requirements for entire process. Further, we identified the digits which were misclassified by the algorithm. Finally, we generate our own test dataset and predict the labels using this system.

One important problem in computer vision is to provide a demographic description a person from an image. In practice, many of the state-of-the-art methods use only an analysis of the face to estimate the age and gender of a person of interest. We present a model that combines two problems, height estimation and demographic classification, which allows each to serve as context for the other. Our idea is to use a calibrated camera for measuring the height of people in the scene. Height is measured by jointly inferring across anthropometric dimensions, age, and gender using publicly available statistics. The height estimate provides context for recognizing the age and gender of the subject, and likewise age and gender conditions the distribution of the anthropometric features for estimating height. The performance of our method is explored on a new database of 127 people captured with a calibrated camera with recorded height, age, and gender. We show that estimating height leads to improvements in age and gender classification, and vice versa. To the best of our knowledge, our model produces the most accurate automatic height estimates reported, with the error having a standard deviation of 26.7 mm.

Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network

Factor analysis aims to reduce the amount of information from the original variable without losing important information. A factor is a collection of reduced variables. Several methods can be used in factor analysis, the most common of which are principal component analysis and maximum likelihood. The principal component analysis technique was used in this study. This technique was chosen because it better represents the purpose of the analysis than others. A case study of beef gravel is used to supplement the explanation of factor analysis using the Principal Component Analysis (PCA) technique. The goal of this study was to identify the most important factors influencing beef gravel quality to maintain product quality. The main factor is the volume of soaking water, and the second is the volume of soaking two dishes. These two factors have a cumulative variance proportion of 55%, indicating that they can influence the quality of the gravel. In contrast, the rest can be influenced by other factors not revealed in the research model.

Human perception of the face involves the observation of both coarse (global) and detailed (local) features of the face to identify and categorize a person. Face categorization involves finding common visual cues, such as gender, race and age, which could be used as a precursor to a face recognition system to improve recognition rates. In this paper, we investigate the fusion of both global and local features for gender classification. Global features are obtained using the principal component analysis (PCA) and discrete cosine transformation (DCT) approaches. A spatial local binary pattern (LBP) approach augmented with a two-dimensional DCT approach has been used to find the local features. The performance of the proposed approach has been investigated through extensive experiments performed on FERET database. The proposed approach gives a recognition accuracy of 98.16% on FERET database. Comparisons with some of the existing techniques have shown a marked reduction in number of features used per image to produce results more efficiently and without loss of accuracy for gender classification.

Urmia Lake in the northwestern of Iran is a hypersaline water body and has become an environmentally important issue especially due to the presence of an infrequent aquatic species, Artemia Urmiana. During the last three decades, several considerable man-made changes including river damming and construction of a causeway across the lake affected the lake salinity. This article aims to propose a new approach of salinity modeling using a reduced-order model based on MIKE21 simulation model, in conjunction with principal component analysis (PCA) technique. At first, spatial variation of salinity in the lake was simulated by MIKE21 to prepare the input information for the PCA. Then, the dominant modes of salinity were determined by PCA technique while MIKE21 simulated results were compared with the output of developed reduced order model. Findings indicated that MIKE21's results closely matched the experimental data collected by field study. Also, the first 10 PCs among 974 modes computed by the reduced order model conserved approximately over 93% of the system variance. Therefore, the reduced order model was sufficient to capture the variation of salinity in the lake using a few first PCs. In other words, it was generally found that improvements in the simulated salinity in the lake provided by reduced order model were comparable to MIKE21 simulations.

Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. We have developed a gender classifier with performance superior to existing gender classifiers. This paper addresses the problem of gender classification using frontal facial images. The testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent component analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. Different classifiers are studied in this lower dimensional subspace. Our experimental results show the best accuracy of 96% in gender classification by combining ICA and support vector machines (SVMs).

FCM algorithm is apt to fall into the local optimization, and what fast FCM algorithm can find optimum is greatly depended on the initialization. PSO-based FCM clustering algorithm avoids the local optima, and also is robust to initialization. The fluctuation however has appeared in the new algorithm, and it had been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. SVM Classifier can handle linear inseparable problems and has the advantages of high accuracy of classification. Motivated by this observation, in this article a new fuzzy clustering technique that SVM Combined with FCM and PSO for Classification Problems has been proposed. Results of numerical experiments on two standard datasets show that the new algorithm is more efficient than the FCM and PSO-based FCM clustering algorithms, it can not only avoids the local optima and is robust to initialization, and also improve accuracy of classification.

In an industrial Avermection bioprocess, there are some different phases and some interims, so using fuzzy clustering technique to partition the whole process and using several models to represent these different phases respectively is more reasonable. Here, we built a fuzzy model for the bioprocess by a mixture method which integrates fuzzy regression clustering technique, genetic programming (GP), genetic algorithm (GA) and interpolation technique. Fuzzy regression clustering technique is used to partition the whole input space into several subspaces based on whether the training data having a similar model which is identified by GP, GA is used to optimizes the parameters of these models and interpolation technique used to define the membership grade for the input data. By this approach, we can fuzzily partition the whole process, find the structures of models which represent subspaces respectively and estimate the parameters simultaneously. Moreover, it has more chance to get a solution with better generalization.