State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy

Face recognition has been a very active research area for past two decades due to its widely applications such as identity authentication, airport security and access control, surveillance, and video retrieval systems, etc. Numerous approaches have been proposed for face recognition and considerable successes have been reported [1]. A successful face recognitio

Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM. Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM dalam menerajui tugas-tugas pengelasan corak. ________________________________________________________________________________________________________________________ Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

A Survey on Emotion Recognition for Human Robot Interaction

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined

A Face Detection and Recognition System for Color Images using Neural Networks with Boosting and Deep Learning

A face detection and recognition system is a biometric identification mechanism which compared to other methods such as finger print identification, speech, signature, hand written and iris recognition, is shown to be more important both theoretically and practically. In principle, the biometric identification methods use a wide range of techniques such as machine learning, computer vision, image processing, pattern recognition and neural networks. The methods have various applications such as in photo and film processing, control access networks, etc. In recent years, the automatic recognition of a human face has become an important problem in pattern recognition. The main reasons are structural similarity of human faces and great impact of illumination conditions, facial expression and face orientation. Face recognition is considered one of the most challenging problems in pattern recognition. A face recognition system consists of two main components, face detection and recognition. In this dissertation a face detection and recognition system using color images with multiple faces is designed, implemented, and evaluated. In color images, the information of skin color is used in order to distinguish between the skin pixels and non-skin pixels, dividing the image into several components. Neural networks and deep learning methods has been used in order to detect skin pixels in the image. A skin database has been built that contains skin pixels from different human skin colors. Information from different color spaces has been used and applied to neural networks. In order to improve system performance, bootstrapping and parallel neural networks with voting have been used. Deep learning has been used as another method for skin detection and compared to other methods. Experiments have shown that in the case of skin detection, deep learning and neural networks methods produce better results in terms of precision and recall compared to the other methods in this field. The step after skin detection is to decide which of these components belong to human face. A template based method has been modified in order to detect the faces. The template is rotated and rescaled to match the component and then the correlation between the template and component is calculated, to determine if the component belongs to a human face. The designed algorithm also succeeds if there are more than one face in the component. A rule based method has been designed in order to detect the eyes and lips in the detected components. After detecting the location of eyes and lips in the component, the face can be detected. After face detection, the faces which were detected in the previous step are to be recognized. Appearance based methods used in this work are one of the most important methods in face recognition due to the robustness of the algorithms to head rotation in the images, noise, low quality images, and other challenges. Different appearance based methods have been designed, implemented and tested. Canonical correlation analysis has been used in order to increase the recognition rate

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

A Two-Stage Classifier Using SVM and RANSAC for Face Recognition

Abstract-A novel face recognition scheme based on twostage classifier, which includes methods of support vector machine (SVM), and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is undertaken by cascade stages. The first stage with OAO-SVM (one-against-one) method picks out two classes with the least variations to the testing images. From the selected two classes, the second stage with "RANSAC" method is used for a fine match with testing images. A fine class with greatest geometric similarity to testing images is thus produced at second stage. This two-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) databases, and the experimental results give evidence that the proposed approach is superior to the previous approaches based on the single classifier and multi-parallel classifier in recognition accuracy

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science