Estimation of image quality factors for face recognition

Over the past few years, verification and identification of humans using biometric has gained attention of researchers and of the public in general. Face recognition systems are used by the public and the government and are applied in different facets of life including security, identification of criminals and identification of terrorists. Because of the importance of these applications, it is of great necessity that face recognition systems be as accurate as possible. Some research has shown that image quality degrades the performance of face recognition systems. Most previous research has focused on designing algorithms for face recognition that deal or compensate a single effect such as blur, lighting conditions, pose, and emotions. In this thesis we identify a number of factors influencing recognition performance and conduct an extensive study of the effects of image quality factors on recognition performance and discuss methods to estimate this quality factors

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Analysis on techniques used to recognize and identifying the Human emotions

Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress of research in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify the proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on the various recognition techniques used to identify the complexity in recognizing the facial expression is presented. This work will also help researchers and scholars to ease out the problem in choosing the techniques used in the identification of the facial expression domain

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

Paralleizing AwSpPCA for robust facial recognition using CUDA

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.Cataloged from PDF version of thesis report.This paper was conducted to analyze the performance benefits of parallelizing the Adaptive Weighted Sub-patterned Principle Component Analysis (Aw SP PCA) algorithm, given that the algorithm is implemented so as to retain the accuracy from its serialized version. The serialized execution of this algorithm is analyzed first and then compared against its parallel implementation, both compiled and run on the same computer. Throughout this paper, the methodology is to undergo a step by step procedure which can clearly outline and describe the problems faced when trying to parallelize this algorithm. It will also describe where, how and why parallelizing procedures were used. The results of the research have shown that while not all parts of the algorithm can be implemented in parallel in the first place, some of the sections that can be parallelized does not necessarily yield a considerable amount of benefits. Also, it was seen that not all sections scale well with problem size, meaning that some portions of the algorithm can be left in its serialized state without much loss in time. The sections which can be parallelized were discussed in detail. Some changes were also made to certain variables to ensure the best accuracy possible. Finally, through analysis and experimentation, a speedup of 2.76 was achieved, with a recognition accuracy of 92.6%.Syed Amer ZawadAshfaque AliB. Computer Science and Engineerin

PCA-ANN Face Recognition System based on Photometric Normalization Techniques

The human face is the main focus of attention in social interaction, and is also the major key in conveying identity and emotion of a person. It has the appealing characteristic of not being intrusive as compared with other biometric techniques. The research works on face recognition started in the 1960s with the pioneering work of Bledsoe and Kanade, wh

Bio : A Mulrimodal biometric authentication system for person identification and verification

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A weighted regional voting based ensemble of multiple classifiers for face recognition.

Face recognition is heavily studied for its wide range of application in areas such as information security, law enforcement, surveillance of the environment, entertainment, smart cards, etc. Competing techniques have been proposed in computer vision conferences and journals, no algorithm has emerged as superior in all cases over the last decade. In this work, we developed a framework which can embed all available algorithms and achieve better results in all cases over the algorithms that we have embedded, without great sacrifice in time complexity. We build on the success of a recently raised concept - Regional Voting. The new system adds weights to different regions of the human face. Different methods of cooperation among algorithms are also proposed. Extensive experiments, carried out on benchmark face databases, show the proposed system's joint contribution from multiple algorithms is faster and more accurate than Regional Voting in every case. --P. ix.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b180553

Pose Invariant Face Recognition Using DT-CWT Partitioning and KPCA

In this paper the suitability of Dual Tree Complex Wavelet Transform for pose invariant Face Recognition is studied and a feature extraction frame work is proposed. This proposed framework will aid in design of Face Recognition system to address the challenging issue like Pose Variation. In contrast to the discrete wavelet Transform (DWT) the design of Dual Tree Complex Wavelet Transform is rugged to shift Invariance and poses good directional properties. These features of DT-CWT motivated to study their suitability for Face Feature Extraction, as the features of face are oriented in different directions. In this proposed frame work the Image is decomposed using DT-CWT and the features are extracted from low frequency band using Kernel Principal Component analysis (KPCA). To show the performance, the proposed method is tested on ORL Database. Satisfactory results are obtained using proposed method compared to existing state of art