Human Face Classification Research Articles

Human Face Classification using TensorFlow and Deployment onto ASIC

In The project aims to develop a human face classification system using TensorFlow and deploying it onto ASIC for Biometrics applications. The Convolutional Neural Networks (CNN) Algorithm is used to classify human faces into predefined categories such as age, gender, and emotion. The CNN model will be trained using a large dataset of labelled images, and the training process will be optimized for ASIC deployment. The trained model will be deployed on an ASIC chip, which is optimized for power and speed. The large dataset will be tested for accuracy and efficiency, and its performance will be evaluated in various engineering applications, such as Security, Biometrics, and Entertainment. The project will demonstrate the feasibility of using TensorFlow Lite and ASIC for developing efficient and accurate human face classification systems for Biometrics applications.

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.

Medical Image Processing, Human Face Classification, Face Recognition and Medical Imaging: Performance Evolution Histogram of Orientation Gradients

In view of the common conditions and the security of the country from all the blackmail that is urging, whetheron the level of the Internet and social communication, there was an urgent need to secure all institutions andalso secure our phones and our possessions from laptops and others using more accurate safety methodsthan these methods I used the method of destination classification, i.e. identifying the destination so that it ispossible to secure We can see you through the fingerprint of the destination, and this method that is popularrecently. In this paper, HoG algorithm was used and this algorithm is considered one of the powerful waysto extract the features that we need in the classification process relying on one of the methods of learning amachine an algorithm was used naive Bayes. After that, the system is evaluated by calculating the accuracy,specificity, and sensitivity and it is found that the efficiency of the system reaches 98%.

A new patch-based LBP with adaptive weights for gender classification of human face

ABSTRACT Recognition methods based on local binary patterns (LBP) depend on a pre-defined structure, such as pixel-to-pixel or pixel-to-patch comparison, for the feature extraction of picture descriptions without the addition of weights. This paper proceeds on the assumption that patch-based LBP should be based on a pyramid structure to enable the computation of gradients using weight parameters pre-defined by eigen theory. The proposed method, namely Adaptive-Weight Patch-based LBP (AWPLBP), seeks to determine the transformation that identifies sets on the hyper-plane capable of maximizing variance in projections. The transformation and quantization loss are used to train the AWPLBP descriptors to facilitate the efficient feature extraction of descriptions. Finally, a classifier with a support vector machine (SVM) is trained using the AWPLBP features for the gender classification of human faces. The experimental results show that the proposed AWPLBP descriptor may achieve better recognition performances than other LBP methods and is competitive with CNN-based methods in gender classification application.

Hyperbolic Wasserstein Distance for Shape Indexing.

Shape space is an active research topic in computer vision and medical imaging fields. The distance defined in a shape space may provide a simple and refined index to represent a unique shape. This work studies the Wasserstein space and proposes a novel framework to compute the Wasserstein distance between general topological surfaces by integrating hyperbolic Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram algorithms. The resulting hyperbolic Wasserstein distance can intrinsically measure the similarity between general topological surfaces. Our proposed algorithms are theoretically rigorous and practically efficient. It has the potential to be a powerful tool for 3D shape indexing research. We tested our algorithm with human face classification and Alzheimer's disease (AD) progression tracking studies. Experimental results demonstrated that our work may provide a succinct and effective shape index.

Hessian regularization by patch alignment framework

In recent years, semi-supervised learning has played a key part in large-scale image management, where usually only a few images are labeled. To address this problem, many representative works have been reported, including transductive SVM, universum SVM, co-training and graph-based methods. The prominent method is the patch alignment framework, which unifies the traditional spectral analysis methods. In this paper, we propose Hessian regression based on the patch alignment framework. In particular, we construct a Hessian using the patch alignment framework and apply it to regression problems. To the best of our knowledge, there is no report on Hessian construction from the patch alignment viewpoint. Compared with the traditional Laplacian regularization, Hessian can better match the data and then leverage the performance. To validate the effectiveness of the proposed method, we conduct human face recognition experiments on a celebrity face dataset. The experimental results demonstrate the superiority of the proposed solution in human face classification.

Human Face Classification using Genetic Algorithm

The paper presents a precise scheme for the development of a human face classification system based human emotion using the genetic algorithm (GA). The main focus is to detect the human face and its facial features and classify the human face based on emotion, but not the interest of face recognition. This research proposed to combine the genetic algorithm and neural network (GANN) for classification approach. There are two way for combining genetic algorithm and neural networks, such as supportive approach and collaborative approach. This research proposed the supportive approach to developing an emotion-based classification system. The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions, such as, mouth (or lip), nose, and eyes. By the analysis of human face, it is seen that most of the emotional changes of the face occurs on eyes and lip. Therefore, two facial feature regions (such as lip and eyes) have been used for emotion-based classification. The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features. To justify the effectiveness of the system, several images were tested. The achievement of this research is higher accuracy rate (about 96.42%) for human frontal face classification based on emotion.

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces

One major challenge in the sensory sciences is to identify the stimulus features on which sensory systems base their computations, and which are predictive of a behavioral decision: they are a prerequisite for computational models of perception. We describe a technique (decision images) for extracting predictive stimulus features using logistic regression. A decision image not only defines a region of interest within a stimulus but is a quantitative template which defines a direction in stimulus space. Decision images thus enable the development of predictive models, as well as the generation of optimized stimuli for subsequent psychophysical investigations. Here we describe our method and apply it to data from a human face classification experiment. We show that decision images are able to predict human responses not only in terms of overall percent correct but also in terms of the probabilities with which individual faces are (mis-) classified by individual observers. We show that the most predictive dimension for gender categorization is neither aligned with the axis defined by the two class-means, nor with the first principal component of all faces-two hypotheses frequently entertained in the literature. Our method can be applied to a wide range of binary classification tasks in vision or other psychophysical contexts.

Human Face Classification Using Ultrasonic Sonar Imaging

In this paper, human face classification using ultrasonic sonar imaging is investigated. On the basis of Freedman's “image pulse” model, the scattering centers model is employed to simplify the complex geometry of the human face into a series of scattering centers. A chirp signal is utilized to detect the human face for its high range resolution and large signal-to-noise ratio. Ultrasonic sonar images, also named high-resolution range profiles, are obtained by demodulating the echoes with a reference chirp signal. Features directly related to the geometry of the human face are extracted from ultrasonic sonar images and verified in the experiments designed with different configurations of transmitter–receiver (TR) pairs. Experimental results indicate that the improved feature extraction method can achieve a high recognition rate of over 99% in the case of ultrasonic transmitters angled at 45° above and orthogonal to the face, and this method improves the performance of ultrasonic face recognition compared with our previous result.

Invariance in radial basis function neural networks in human face classification

This paper is concerned with the types of invariance exhibited by Radial Basis Function (RBF) neural networks when used for human face classification, and the generalisation abilities arising from this behaviour. Experiments using face images in ranges from face-on to profile show the RBF network's invariance to 2-D shift, scale and y-axis rotation. Finally, the suitability of RBF techniques for future, more automated face classification purposes is discussed.