Real-Time Face Detection and Recognition on LEGO Mindstorms NXT Robot

In the biometrics community, face and speaker recognition are mature fields in which several systems have been proposed over the past twenty years. While existing systems perform well under controlled recording conditions, mismatch caused by the use of different sensors or a lack of cooperation from the subject still significantly affects performance, especially in challenging scenarios such as in forensics. Furthermore, existing methods suffer from scalability issues, which prevents them from taking advantage of increasingly large amounts of training data. This is otherwise a promising approach to improve accuracy in such challenging scenarios. In this thesis we address these problems of mismatch and complexity by developing scalable probabilistic models that we apply to face, speaker and bimodal recognition. Our contributions are four-fold. First, we propose a unified framework for session variability modeling techniques based on Gaussian mixture models (GMM), that encompasses inter-session variability (ISV) modeling, joint factor analysis (JFA) and total variability (TV) modeling. Second, we propose a novel exact and scalable formulation of probabilistic linear discriminant analysis (PLDA), which is a probabilistic and generative framework that models between-class and within-class variations. This formulation solves a major scalability issue, by improving both the time complexity of the training procedure from cubic to linear with respect to the number of samples per class, and the complexity of the scoring procedure. Furthermore, the implementations of all the proposed techniques are integrated into a novel collaborative open source software library called Bob 1 that enforces fair evaluations and encourages reproducible research. Fourth and finally, large-scale experiments are conducted with all of the above machine learning algorithms on several databases such as FRGC for face recognition, NIST SRE12 for speaker recognition and MOBIO for bimodal recognition, showing competitive performances.

The primary goal of this study is to create a robot that can monitor human activity by using wireless night vision cameras and Using an Android Bluetooth application, robots can be manually controlled. A night vision wireless camera built into the robot can send videos of people. This robot’s job is to go around and collect audio and video data from the environment it is in, then communicate that data to the user. In this project, a mobile device is used to control the robot. This robot can send data to a distant IoT cloud database after collecting it from a faraway location. By transmitting commands via the Bluetooth software on our Android phone, we can direct the robot’s movement. In the proposed system, we are going to construct a low-cost Microcontroller Based Android operated Robot that can move in any of the directions of Forward, Left, Right, Backward, and Stop. The robot will travel in the directions indicated by the mobile device, including forward, backward, left, and right. There are numerous uses for this system. The goal of our project is to create a robotic vehicle with a wireless camera capable of night vision for remote monitoring/spying applications. Even in dimly lit areas, the night vision camera enables the transmission of real-time night vision footage. The camera’s footage can be watched on a PC for reference.

Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper, we have proposed the boosting method for face recognition (FR) that improves the linear discriminant analysis (LDA)-based technique. The improvement is achieved by incorporating the regularized LDA (R-LDA) technique into the boosting framework. R-LDA is based on a new regularized Fisher’s discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. The AdaBoost technique is utilized within this framework to generalize a set of simple FR subproblems and their corresponding LDA solutions and combines the results from the multiple, relatively weak, LDA solutions to form a strong solution. The comparative experimental result on FERET database demonstrates that the proposed boosting method achieves more accurate results over the individual algorithms.

ldquoMechatronicrdquo is a term that is used for describe an integrating science of the mechanical, electronic and computers areas with the objective of generating intelligent mechanical products. This characteristic needs to be taken into account for choosing a working platform when practical works with students are developed. For this, in this paper a platform based on LEGO Mindstorms NXT is proposed. Since it is a very flexible, easy to use and powerful environment, many programming and robot control activities can be developed. This allows the implementation of different controllers, working with artificial vision algorithms and images treatment, movements planning, avoiding obstacles, etc.

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

A novel Bayesian logistic discriminant model: An application to face recognition

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Face recognition using LDA mixture model

A multi-robot stage production is novel and challenging as different robots have to communicate and coordinate to produce a smooth performance. We made a multi-robot stage production possible using the NAO humanoid robots and the Lego Mindstorms NXT robots with a group of undergraduate women who had programming experience, but little experience with robots. The undergraduates from around the world were participating in a three day workshop – Opportunities for Undergraduate Research in Computer Science (OurCS), organized by the School of Computer Science from Carnegie Mellon University that provide opportunities for these undergraduates to work on computing-related research problems. They were given twelve and a half hours over a span of three days to familiarize themselves with the robots, plan the storyboard of the performance, program the robots, generate a multi-robot performance and create a presentation on what they learned and did. In this paper, we describe the tools and infrastructure we created to support the creation of a multi-robot stage production within the allocated time and explain how the time in the workshop was allocated to enable the undergraduates to complete the multi-robot stage production.

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA share common goals and methodologies, in previous research they have been applied separately and independently. In this paper, we propose an extension of LDA to multiple factor frameworks. Our proposed method, Multifactor Discriminant Analysis, aims to obtain multilinear projections that maximize the between-class scatter while minimizing the withinclass scatter, which is the same core fundamental objective of LDA. Moreover, Multifactor Discriminant Analysis (MDA), like MPCA, uses multifactor analysis and calculates subject parameters that represent the characteristics of subjects and are invariant to other changes, such as viewpoints or lighting conditions. In this way, our proposed MDA combines the best virtues of both LDA and MPCA for face recognition.

The face recognition performance of linear discriminant analysis (LDA) is considerably reduced by illumination and scale changes. Actually, performance of LDA is significantly affected by the number of images of each individual in the training set. Hence, the idea presented in this paper is to generate new images with illumination and scale changes, from the available frontal images in the training set, and add to training set such that the recognition rate of LDA is maximised over both the training and test sets. A continuous genetic algorithm is used to find the optimal sets of illumination and scale change parameters that make the success of LDA invariant to these facial variations. Compared to the application of LDA over the initially given face database, experimental evaluations demonstrated that the performance of LDA is significantly enhanced over the test set for illumination and scale changes, without decreasing the success for other test cases.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Employing fisher discriminant analysis for Arabic text classification

Face recognition in artificial intelligence is a frequent problem. This application was extensively used in our daily life. Several smartphones were opening phones with facial identification to safeguard private details and used on Facebook to identify instantly when users of Facebook appear in pictures. Several approaches for face recognition have already been suggested until now, but it remains very difficult in real-world situations. A primary technique to differentiate people depends on various conditions, for example, partial facial occlusion, illumination, and posture variety. This paper aims at designing a face recognition approach utilizing machine learning algorithm and principal component analysis (PCA). It is experimented using linear discriminant analysis, multilayer perceptron, Naive Bayes, and support vector machine. Moreover, it has achieved recognition accuracy of 97% & 100% by using PCA and linear discriminant analysis.

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural network classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.