Proof-of-Concept

Biometry is an area in great expansion and is considered as possible solution to cases where high authentication parameters are required. Although this area is quite advanced in theoretical terms, using it in practical terms still carries some problems. The systems available still depend on a high cooperation level to achieve acceptable performance levels, which was the backdrop to the development of the following project. By studying the state of the art, we propose the creation of a new and less cooperative biometric system that reaches acceptable performance levels.A constante necessidade de parâmetros mais elevados de segurança, nomeadamente ao nível de autenticação, leva ao estudo biometria como possível solução. Actualmente os mecanismos existentes nesta área tem por base o conhecimento de algo que se sabe ”password” ou algo que se possui ”codigo Pin”. Contudo este tipo de informação é facilmente corrompida ou contornada. Desta forma a biometria é vista como uma solução mais robusta, pois garante que a autenticação seja feita com base em medidas físicas ou compartimentais que definem algo que a pessoa é ou faz (”who you are” ou ”what you do”). Sendo a biometria uma solução bastante promissora na autenticação de indivíduos, é cada vez mais comum o aparecimento de novos sistemas biométricos. Estes sistemas recorrem a medidas físicas ou comportamentais, de forma a possibilitar uma autenticação (reconhecimento) com um grau de certeza bastante considerável. O reconhecimento com base no movimento do corpo humano (gait), feições da face ou padrões estruturais da íris, são alguns exemplos de fontes de informação em que os sistemas actuais se podem basear. Contudo, e apesar de provarem um bom desempenho no papel de agentes de reconhecimento autónomo, ainda estão muito dependentes a nível de cooperação exigida. Tendo isto em conta, e tudo o que já existe no ramo do reconhecimento biometrico, esta área está a dar passos no sentido de tornar os seus métodos o menos cooperativos poss??veis. Possibilitando deste modo alargar os seus objectivos para além da mera autenticação em ambientes controlados, para casos de vigilância e controlo em ambientes não cooperativos (e.g. motins, assaltos, aeroportos). É nesta perspectiva que o seguinte projecto surge. Através do estudo do estado da arte, pretende provar que é possível criar um sistema capaz de agir perante ambientes menos cooperativos, sendo capaz de detectar e reconhecer uma pessoa que se apresente ao seu alcance.O sistema proposto PAIRS (Periocular and Iris Recognition Systema) tal como nome indica, efectua o reconhecimento através de informação extraída da íris e da região periocular (região circundante aos olhos). O sistema é construído com base em quatro etapas: captura de dados, pré-processamento, extração de características e reconhecimento. Na etapa de captura de dados, foi montado um dispositivo de aquisição de imagens com alta resolução com a capacidade de capturar no espectro NIR (Near-Infra-Red). A captura de imagens neste espectro tem como principal linha de conta, o favorecimento do reconhecimento através da íris, visto que a captura de imagens sobre o espectro visível seria mais sensível a variações da luz ambiente. Posteriormente a etapa de pré-processamento implementada, incorpora todos os módulos do sistema responsáveis pela detecção do utilizador, avaliação de qualidade de imagem e segmentação da íris. O modulo de detecção é responsável pelo desencadear de todo o processo, uma vez que esta é responsável pela verificação da exist?ncia de um pessoa em cena. Verificada a sua exist?ncia, são localizadas as regiões de interesse correspondentes ? íris e ao periocular, sendo também verificada a qualidade com que estas foram adquiridas. Concluídas estas etapas, a íris do olho esquerdo é segmentada e normalizada. Posteriormente e com base em vários descritores, é extraída a informação biométrica das regiões de interesse encontradas, e é criado um vector de características biométricas. Por fim, é efectuada a comparação dos dados biometricos recolhidos, com os já armazenados na base de dados, possibilitando a criação de uma lista com os níveis de semelhança em termos biometricos, obtendo assim um resposta final do sistema. Concluída a implementação do sistema, foi adquirido um conjunto de imagens capturadas através do sistema implementado, com a participação de um grupo de voluntários. Este conjunto de imagens permitiu efectuar alguns testes de desempenho, verificar e afinar alguns parâmetros, e proceder a optimização das componentes de extração de características e reconhecimento do sistema. Analisados os resultados foi possível provar que o sistema proposto tem a capacidade de exercer as suas funções perante condições menos cooperativas

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

Iris Recognition System based on ZM, GF, VR and Matching Level Fusion

Isis is the physiological biometric trait used to recognized a person efficiently. In this paper, we propose Iris Recognition System based on ZM, GF, VR and Matching Level Fusion. The Region of Interest (ROI) of iris is extracted using segmentation. Zernike Moments (ZM) is applied on segmented iris images to extract ZM features. The novel concept of many feature vectors of a single person are converted into single vector per person ie., Vectors Reduction (VR). The Euclidian Distance (ED) is used to compare feature vectors in the database with feature vectors in test section to compute the performance parameters. The Gabor Filter (GF) is also used to extract features of iris. Many GF feature vectors of single person are connected into single feature vector per person. The ED is used to compare database and test feature vectors to compute performance parameters. The performance parameters obtained from ZM and GF are fused using normalization technique to improve the performance parameters. It is observed that, the performance parameters are better compared to existing techniques

Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System

Face recognition is considered to be one of the most reliable biometrics where security issues are of concerned. Feature extraction which is a functional block of a face recognition system becomes a critical problem when there is need to obtain the best feature with minimum classification error and low running time. Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these systems remain an open problem. Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component Analysis) for face recognition system. A database of 360 face images was created by obtaining facial images from LAUTECH Biometric Research Group consisting of six facial expressions of 60 persons. Images were preprocessed (gray scaling, cropping and histogram equalization) and the kernel variants were used to extract distinctive features and reduce the dimensionality of each of the images from 600x800 pixels to four smaller dimensions: 50x50, 100x100, 150x150 and 200x200 pixels. Euclidean Distance similarity measure was used for classification. The performance of the three kernel variants was evaluated for face recognition system using 180 images for training and 180 images for testing using the following metrics: Recognition Accuracy (RA) and Recognition Time (RT). Empirical result indicate that KLDA performs best for face recognition system with an average accuracy of 94.52%.  The larger image dimension also results in better recognition performance. We intend to experiment on other classifiers for face recognition system in our future work. Keywords— Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed

Sparse Methods for Robust and Efficient Visual Recognition

Visual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust visual recognition is still an open problem. In recent years, sparse representation-based methods have become popular for visual recognition. By learning a compact dictionary of data and exploiting the notion of sparsity, start-of-the-art results have been obtained on many recognition tasks. However, existing data-driven sparse model techniques may not be optimal for some challenging recognition problems. In this dissertation, we consider some of these recognition tasks and present approaches based on sparse coding for robust and efficient recognition in such cases. First we study the problem of low-resolution face recognition. This is a challenging problem, and methods have been proposed using super-resolution and machine learning based techniques. However, these methods cannot handle variations like illumination changes which can happen at low resolutions, and degrade the performance. We propose a generative approach for classifying low resolution faces, by exploiting 3D face models. Further, we propose a joint sparse coding framework for robust classification at low resolutions. The effectiveness of the method is demonstrated on different face datasets. In the second part, we study a robust feature-level fusion method for multimodal biometric recognition. Although score-level and decision-level fusion methods exist in biometric literature, feature-level fusion is challenging due to different output formats of biometric modalities. In this work, we propose a novel sparse representation-based method for multimodal fusion, and present experimental results for a large multimodal dataset. Robustness to noise and occlusion are demonstrated. In the third part, we consider the problem of domain adaptation, where we want to learn effective classifiers for cases where the test images come from a different distribution than the training data. Typically, due to high cost of human annotation, very few labeled samples are available for images in the test domain. Specifically, we study the problem of adapting sparse dictionary-based classification methods for such cases. We describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both domains in the projected low dimensional space. The proposed method is efficient and performs on par or better than many competing state-of-the-art methods. Lastly, we study an emerging analysis framework of sparse coding for image classification. We show that the analysis sparse coding can give similar performance as the typical synthesis sparse coding methods, while being much faster at sparse encoding. In the end, we conclude the dissertation with discussions and possible future directions

Gait Analysis and Recognition for Automated Visual Surveillance

Human motion analysis has received a great attention from researchers in the last decade due to its potential use in different applications such as automated visual surveillance. This field of research focuses on the perception and recognition of human activities, including people identification. We explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a successful detection rate of 100%. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people. Furthermore, we propose a new approach to extract human joints (vertex positions) using a model-based method. The spatial templates describing the human gait motion are produced via gait analysis performed on data collected from manual labeling. The Elliptic Fourier Descriptors are used to represent the motion models in a parametric form. The heel strike data is exploited to reduce the dimensionality of the parametric models. People walk normal to the viewing plane, as major gait information is available in a sagittal view. The ankle, knee and hip joints are successfully extracted with high accuracy for indoor and outdoor data. In this way, we have established a baseline analysis which can be deployed in recognition, marker-less analysis and other areas. The experimental results confirmed the robustness of the model-based approach to recognise walking subjects with a correct classification rate of 95% using purely the dynamic features derived from the joint motion. Therefore, this confirms the early psychological theories claiming that the discriminative features for motion perception and people recognition are embedded in gait kinematics. Furthermore, to quantify the intrusive nature of gait recognition we explore the effects of the different covariate factors on the performance of gait recognition. The covariate factors include footwear, clothing, carrying conditions and walking speed. As far as the author can determine, this is the first major study of its kind in this field to analyse the covariate factors using a model-based method

A Highly Efficient Biometrics Approach for Unconstrained Iris Segmentation and Recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineats the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design