Face Detection using Min-Max Features Enhanced with Locally Linear Embedding

Face detection is critical function in many embedded applications such as computer vision and security as it is widely used as preprocessor for face recognition systems. As a preprocessor, the face detection system needs to extract features from a region of interest and classify them quickly as either face or non-face. In our previous works, we have devised a feature representation method called Min-Max (MMX) feature that allows representation of a region of interest using a few data points based on the unique characteristics of vertical and horizontal summation of face regions. In this paper, we attempt to improve the classification accuracy of MMX by integrating a technique called Locally Linear Embedding (LLE), a powerful dimensionality and feature enhancement algorithm that has been used successfully in many pattern recognition tasks. To test the performance of the proposed enhancement, the LLE-treated features were compared with non-treated features using a Multi-Layer Perceptron (MLP) neural network classifier. The results indicate an increase (+1.2%) in classification accuracy of the MLPs, demonstrating the ability of LLE to enhance the representation of MMX features

Temporal - spatial recognizer for multi-label data

Pattern recognition is an important artificial intelligence task with practical applications in many fields such as medical and species distribution. Such application involves overlapping data points which are demonstrated in the multi- label dataset. Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. Existing recognition methods suffer from sensitivity to noise and overlapping points as they could not recognize a pattern when there is a shift in the position of the data points. Furthermore, the methods do not implicate temporal information in the process of recognition, which leads to low quality of data clustering. In this study, an improved pattern recognition method based on Hierarchical Temporal Memory (HTM) is proposed to solve the overlapping in data points of multi- label dataset. The imHTM (Improved HTM) method includes improvement in two of its components; feature extraction and data clustering. The first improvement is realized as TS-Layer Neocognitron algorithm which solves the shift in position problem in feature extraction phase. On the other hand, the data clustering step, has two improvements, TFCM and cFCM (TFCM with limit- Chebyshev distance metric) that allows the overlapped data points which occur in patterns to be separated correctly into the relevant clusters by temporal clustering. Experiments on five datasets were conducted to compare the proposed method (imHTM) against statistical, template and structural pattern recognition methods. The results showed that the percentage of success in recognition accuracy is 99% as compared with the template matching method (Featured-Based Approach, Area-Based Approach), statistical method (Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machines and Neural Network) and structural method (original HTM). The findings indicate that the improved HTM can give an optimum pattern recognition accuracy, especially the ones in multi- label dataset

Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition

Over the last decade, research has focused on machine learning and data mining to develop frameworks that can improve data analysis and output performance; to build accurate decision support systems that benefit from real-life datasets. This leads to the field of clinical data analysis, which has attracted a significant amount of interest in the computing, information systems, and medical fields. To create and develop models by machine learning algorithms, there is a need for a particular type of data for the existing algorithms to build an efficient model. Clinical datasets pose several issues that can affect the classification of the dataset: missing values, high dimensionality, and class imbalance. In order to build a framework for mining the data, it is necessary first to preprocess data, by eliminating patients’ records that have too many missing values, imputing missing values, addressing high dimensionality, and classifying the data for decision support.This thesis investigates a real clinical dataset to solve their challenges. Autoencoder is employed as a tool that can compress data mining methodology, by extracting features and classifying data in one model. The first step in data mining methodology is to impute missing values, so several imputation methods are analysed and employed. Then high dimensionality is demonstrated and used to discard irrelevant and redundant features, in order to improve prediction accuracy and reduce computational complexity. Class imbalance is manipulated to investigate the effect on feature selection algorithms and classification algorithms.The first stage of analysis is to investigate the role of the missing values. Results found that techniques based on class separation will outperform other techniques in predictive ability. The next stage is to investigate the high dimensionality and a class imbalance. However it was found a small set of features that can improve the classification performance, the balancing class does not affect the performance as much as imbalance class

A novel face recognition system in unconstrained environments using a convolutional neural network

The performance of most face recognition systems (FRS) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be due to the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRS, thus limiting the wide applicability of most FRS in real-life scenarios. Thus, it is envisaged that developing more effective pre-processing techniques, in addition to selecting the correct features for classification, will significantly improve the performance of FRS. The thesis investigates different research works on FRS, its techniques and challenges in unconstrained environments. The thesis proposes a novel image enhancement technique as a pre-processing approach for FRS. The proposed enhancement technique improves on the overall FRS model resulting into an increased recognition performance. Also, a selection of novel hybrid features has been presented that is extracted from the enhanced facial images within the dataset to improve recognition performance. The thesis proposes a novel evaluation function as a component within the image enhancement technique to improve face recognition in unconstrained environments. Also, a defined scale mechanism was designed within the evaluation function to evaluate the enhanced images such that extreme values depict too dark or too bright images. The proposed algorithm enables the system to automatically select the most appropriate enhanced face image without human intervention. Evaluation of the proposed algorithm was done using standard parameters, where it is demonstrated to outperform existing image enhancement techniques both quantitatively and qualitatively. The thesis confirms the effectiveness of the proposed image enhancement technique towards face recognition in unconstrained environments using the convolutional neural network. Furthermore, the thesis presents a selection of hybrid features from the enhanced image that results in effective image classification. Different face datasets were selected where each face image was enhanced using the proposed and existing image enhancement technique prior to the selection of features and classification task. Experiments on the different face datasets showed increased and better performance using the proposed approach. The thesis shows that putting an effective image enhancement technique as a preprocessing approach can improve the performance of FRS as compared to using unenhanced face images. Also, the right features to be extracted from the enhanced face dataset as been shown to be an important factor for the improvement of FRS. The thesis made use of standard face datasets to confirm the effectiveness of the proposed method. On the LFW face dataset, an improved performance recognition rate was obtained when considering all the facial conditions within the face dataset.Thesis (PhD)--University of Pretoria, 2018.CSIR-DST Inter programme bursaryElectrical, Electronic and Computer EngineeringPhDUnrestricte

Characterization and Optimization of Radiation at Nano Scale: Applications in Solar Cell Design

High energy needs and environmental concerns associated with fossil fuels have raised the demand for efficient and clean alternatives of power generation. Solar cell technology is one of the most promising options of reliable renewable power sources despite high costs. Thin film solar cells offer the potential for reduction in the cost per kilowatt-hour due to the lower material usage. Nevertheless, most thin film solar cells suffer from low efficiency, though advancements in the science of near field radiation have led to substantial improvements in their optical efficiency. Many design challenges remain to be overcome for the wide-scale commercialization of thin film solar cells. In this dissertation, a numerical study is conducted for optical, optoelectrical and scattering performance enhancement of subwavelength optical devices (i.e., thin film solar cells and light trapping nanoparticles). The proposed design framework of thin film solar cells is based on learning based optimization and characterization methods, which utilize approximations of time consuming simulations. Additionally, a free form nanoparticle design procedure using evolutionary shape optimization is detailed. The background of thin film solar cells and a comprehensive literature review of the thin film solar cell design approaches are provided in Chapters 2 and 3, respectively. The optical enhancement of thin film solar cells using nanoparticles with different shapes is studied in Chapter 4. In Chapter 5, an approximate formulation for optoelectrical efficiency of thin film solar cells is developed to accelerate the design optimization. The learning based design methodology that is introduced in Chapter 5 is further improved in Chapter 6 using a knowledge transfer concept (also known as transfer learning). In this chapter, multiple sets of material combinations are optimized and compared with each other in terms of their optoelectrical efficiencies. In Chapter 7, nanoparticles are designed for maximum scattering, which is desired for enhanced optical performance, using a nonparametric evolutionary design method. In Chapter 8, a predictive model for scattering of arbitrarily shaped nanoparticles using descriptive geometric features is proposed. Overall, this dissertation has led to significant contributions in the field of thin film solar cell design. The results show that the computational burden of the thin film solar cell design can be overcome significantly without sacrificing accuracy. Furthermore, the design methods developed for this dissertation can easily be transferred to other engineering areas involving repetitive, time consuming simulations for design optimization, such as other photonic design problems and integrated circuit design