An Exploration of the Feasibility of FPGA Implementation of Face Recognition Using Eigenfaces

Biometric identification has been a major force since 1990\u27s. There are different types of approaches for it; one of the most significant approaches is face recognition. Over the past two decades, face recognition techniques have improved significantly, the main focus being the development of efficient algorithm. The state of art algorithms with good recognition rate are implemented using programming languages such as C++, JAVA and MATLAB, these requires a fast and computationally efficient hardware such as workstations. If the face recognition algorithms could be written in a Hardware Description Language, they could be implemented in an FPGA. In this thesis we have choose the eigenfaces algorithm, since it is simple and very efficient, this algorithm is first solved analytically, and then the architecture is designed for FPGA implementation. We then develop the Verilog module for each of these modules and test their functionality using a Verilog Simulator and finally we discuss the feasibility of FPGA implementation. Implementing the face recognition technology in an FPGA would mean that they would require relatively low power and the size is drastically reduced when compared to the workstations. They would also be much faster and efficient, since they are specifically designed for face recognition

Hybrid component-based face recognition.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Facial recognition (FR) is the trusted biometric method for authentication. Compared to other biometrics such as signature; which can be compromised, facial recognition is non-intrusive and it can be apprehended at a distance in a concealed manner. It has a significant role in conveying the identity of a person in social interaction and its performance largely depends on a variety of factors such as illumination, facial pose, expression, age span, hair, facial wear, and motion. In the light of these considerations this dissertation proposes a hybrid component-based approach that seeks to utilise any successfully detected components. This research proposes a facial recognition technique to recognize faces at component level. It employs the texture descriptors Grey-Level Co-occurrence (GLCM), Gabor Filters, Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transforms (SIFT), and the shape descriptor Zernike Moments. The advantage of using the texture attributes is their simplicity. However, they cannot completely characterise the whole face recognition, hence the Zernike Moments descriptor was used to compute the shape properties of the selected facial components. These descriptors are effective facial components feature representations and are robust to illumination and pose changes. Experiments were performed on four different state of the art facial databases, the FERET, FEI, SCface and CMU and Error-Correcting Output Code (ECOC) was used for classification. The results show that component-based facial recognition is more effective than whole face and the proposed methods achieve 98.75% of recognition accuracy rate. This approach performs well compared to other componentbased facial recognition approaches

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

A new approach to face recognition using Curvelet Transform

Multiresolution tools have been profusely employed in face recognition. Wavelet Transform is the best known among these multiresolution tools and is widely used for identification of human faces. Of late, following the success of wavelets a number of new multiresolution tools have been developed. Curvelet Transform is a recent addition to that list. It has better directional ability and effective curved edge representation capability. These two properties make curvelet transform a powerful weapon for extracting edge information from facial images. Our work aims at exploring the possibilities of curvelet transform for feature extraction from human faces in order to introduce a new alternative approach towards face recognition

Real-Time Deep Learning-Based Face Recognition System

This research proposes Real-time Deep Learning-based Face recognition algorithms using MATLAB and Python. Generally, Face recognition is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, etc. The facial recognition system can be built by following two steps. In the first step, the facial features are picked up or extracted, then the second step involves pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made more progress in face recognition technology. Convolution Neural Network is one among the Deep Learning approaches and has shown excellent performance in many fields, such as image recognition of a large amount of training data (such as ImageNet). However, due to hardware limitations and insufficient training datasets, high performance is not achieved. Therefore, in this work, the Transfer Learning method is used to improve the performance of the face-recognition system even for a smaller number of images. For this, two pre-trained models, namely, GoogLeNet CNN (in MATLAB) and FaceNet (in Python) are used. Transfer learning is used to perform fine-tuning on the last layer of CNN model for new classification tasks. FaceNet presents a unified system for face verification (is this the same person?), recognition (who is this person?) and clustering (finds common people among these faces) using the method based on learning a Euclidean embedding per image using a deep convolutional network

Analysis of Parallel SOC Architectural Characteristics for Accelerating Face Identification

Growing worldwide concerns about terrorism have increased interest in rapidly and accurately identifying individuals such as potential terrorists. The ability to quickly screen an individual against the more than one million entries on the Terrorist Watch List using face identification could significantly improve national security and other security screening applications. Top accuracy face identification algorithms are not real-time. The top face identification algorithms evaluated in National Institutes of Standards (NIST) testing achieve 95% or greater identification accuracy but require several minutes to complete identification on a 1,196 member gallery set of 100 kilopixel resolution images. Recent testing shows that face identification algorithms are significantly slower for current NIST test sets with a 14,365 member gallery set of 4 megapixel images. Significant performance improvement is needed to match a one million member gallery set. The International Technology Roadmap for Semiconductors projects Systems on a Chip with more than one thousand processors will be available within ten years. However, it’s not clear how face identification algorithms can use these massively parallel SOCs to improve performance or which architectural characteristics are important for these algorithms. This research specifies key architectural characteristics for a massively parallel SOC to enable real-time face identification. A set of face identification benchmarks has been created to guide this research and includes small and large image data sets. This research contributes a method to explore the SOC design space to evaluate the final SOC performance. Specifically, this research is focused on the impact of processor instruction set architecture performance, the external memory bandwidth, the quantity of processing cores, the on-chip communication network, and the mapping of the face identification benchmarks

A Software Engineered Voice-Enabled Job Recruitment Portal System

The inability of job seekers to get timely job information regarding the status of the application submitted via conventional job portal system which is usually dependent on accessibility to the Internet has made so many job applicants to lose their placements. Worse still, the epileptic services offered by Internet Service Providers and the poor infrastructures in most developing countries have greatly hindered the expected benefits from Internet usage. These have led to cases of online vacancies notifications unattended to simply because a job seeker is neither aware nor has access to the Internet. With an increasing patronage of mobile phones, a self-service job vacancy notification with audio functionality or an automated job vacancy notification to all qualified job seekers through mobile phones will simply provide a solution to these challenges. In this paper, we present a Voice-enabled Job Recruitment Portal (JRP) System. The system is accessed through two interfaces – the voice user’s interface (VUI) and web interface. The VUI was developed using VoiceXML and the web interface using PHP, and both interfaces integrated with Apache and MySQL as the middleware and back-end component respectively. The JRP proposed in this paper takes the hassle of job hunting from job seekers, provides job status information in real-time to the job seeker and offers other benefits such as, cost, effectiveness, speed, accuracy, ease of documentation, convenience and better logistics to the employer in seeking the right candidate for a job

Machine learning techniques in pain recognition.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b131711

DRUBIS : a distributed face-identification experimentation framework - design, implementation and performance issues

We report on the design, implementation and performance issues of the DRUBIS (Distributed Rhodes University Biometric Identification System) experimentation framework. The Principal Component Analysis (PCA) face-recognition approach is used as a case study. DRUBIS is a flexible experimentation framework, distributed over a number of modules that are easily pluggable and swappable, allowing for the easy construction of prototype systems. Web services are the logical means of distributing DRUBIS components and a number of prototype applications have been implemented from this framework. Different popular PCA face-recognition related experiments were used to evaluate our experimentation framework. We extract recognition performance measures from these experiments. In particular, we use the framework for a more indepth study of the suitability of the DFFS (Difference From Face Space) metric as a means for image classification in the area of race and gender determination