120 research outputs found

    Facial analysis in video : detection and recognition

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    Biometric authentication systems automatically identify or verify individuals using physiological (e.g., face, fingerprint, hand geometry, retina scan) or behavioral (e.g., speaking pattern, signature, keystroke dynamics) characteristics. Among these biometrics, facial patterns have the major advantage of being the least intrusive. Automatic face recognition systems thus have great potential in a wide spectrum of application areas. Focusing on facial analysis, this dissertation presents a face detection method and numerous feature extraction methods for face recognition. Concerning face detection, a video-based frontal face detection method has been developed using motion analysis and color information to derive field of interests, and distribution-based distance (DBD) and support vector machine (SVM) for classification. When applied to 92 still images (containing 282 faces), this method achieves 98.2% face detection rate with two false detections, a performance comparable to the state-of-the-art face detection methods; when applied to videQ streams, this method detects faces reliably and efficiently. Regarding face recognition, extensive assessments of face recognition performance in twelve color spaces have been performed, and a color feature extraction method defined by color component images across different color spaces is shown to help improve the baseline performance of the Face Recognition Grand Challenge (FRGC) problems. The experimental results show that some color configurations, such as YV in the YUV color space and YJ in the YIQ color space, help improve face recognition performance. Based on these improved results, a novel feature extraction method implementing genetic algorithms (GAs) and the Fisher linear discriminant (FLD) is designed to derive the optimal discriminating features that lead to an effective image representation for face recognition. This method noticeably improves FRGC ver1.0 Experiment 4 baseline recognition rate from 37% to 73%, and significantly elevates FRGC xxxx Experiment 4 baseline verification rate from 12% to 69%. Finally, four two-dimensional (2D) convolution filters are derived for feature extraction, and a 2D+3D face recognition system implementing both 2D and 3D imaging modalities is designed to address the FRGC problems. This method improves FRGC ver2.0 Experiment 3 baseline performance from 54% to 72%

    A Subspace Projection Methodology for Nonlinear Manifold Based Face Recognition

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    A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations. Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective

    Ear Biometrics In Personal Identification

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    Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu tez biyometrik tabanlı, kimlik tespitine dayalı güvenlik sistemi geliştirme fikriyle başlayan çalışmanın bir parçasıdır. Günümüzde, biyometrik tabanlı ve türleri içinde en yüksek doğruluk oranına sahip parmak izi ve iris tarama yöntemleri kriminal vakalarda ve yüksek güvenlik gerektiren tesislerde kullanılmaktatır. Yüz tanıma hala gelişmekte olan bir biyometrik yöntemidir, fakat yapılan literatür araştırmalarında ortam ışıklandırması, makyaj, verilen poz, duygusal ifadeler ve estetik operasyonlar gibi yüz görünümü üzerinde etkisi olan faktörlerin yüz tanıma probleminde doğrudan yöntemlerin başarımını azaltacak yönde etkili olduğu görülmüştür. Dolayısıyla, yüz gibi erişimi kolay fakat onun gibi gündelik hayatın makyaj, duygusal ifadeler, bıyık ve sakal bırakma gibi faktörlerinden etkilenmeyecek bir biyometrik gereksinimi ortaya çıkmıştır. Alternatif biyometriğin başarımının yüzle kıyaslanabilir mertebelerde olması gerektiği açıktır. Araştırmaların devamında, tek yumurta ikizlerinin birbirlerine ne kadar benzeseler de kulak yapılarının farklı olduğu, kulağın 3 boyutlu olsa da yüz kadar detay içermediği ve kulağın yapısı itibariyle duygusal açılımlar ifade edilirken biçimini değiştirmediği görülmüştür. Bunların ışığında, kulak yüze karşı güçlü bir alternatif biyometrik olarak ortaya çıkmaktadır. Bu çalışmada, literatürde önerilen yöntemler kulak resimleri üzerine uygulanmıştır. Bu yöntemler veri kümesi olarak 2 boyutlu resimleri kullanan ve veri kümesi üzerinde sınıflandırma yapan, lineer yöntemlerdir. Yapılan çalışma sonunda görülmüştür ki, PCA, FLD, FLD nın geliştirilmesiyle oluşturulan DCVA ve LPP yöntemlerinin kulak tanımadaki başarımları yüz tanımadaki başarılarından daha yüksektir. Bu yöntemlerin kulak tanımadaki doğru eşleştirme oranları, literatürde bulunan, yüz tanımadaki eşleştirme oranlarıyla karşılaştırıldıklarında daha yüksektir. Yapılan bu çalışmanın sonuçları biyometrik tabanlı kimlik tesbit yöntemleri için kulağın yüzden daha iyi bir alternatif olduğunu göstermiştir.This thesis is one of the parts of a biometric based identity verification security system development project. Today, the most successful biometric based identification technologies such as fingerprint and iris scan are used worldwide in both criminal investigations and high security facilities. Face recognition is one of the developing biometric methods; however illumination, makeup, posing, emotional expressions and face-lifting reduce the success of face recognition. A new biometric which is not effected by any of the factors above is needed. The new biometric should be as successful as face recognition. Twins are identical but their ears differ from each other, ear is also 3-dimensional but it is simpler than face and emotional expressions do not affect the ear. In the light of this, ear is a good alternative to face, as a biometric. In this study, the methods presented in the literature are tested on ear images. These methods are linear classification algorithms that work on 2D image databases. It is found out that, PCA, FLD, modified FLD which is also known as DCVA and LPP has better results at ear recognition than face recognition. Ear recognition has higher hit rates, when compared with face recognition researches that are presented in the literature previously. The results of this study proved that ear is the best alternative to face at personal identification tasks.Yüksek LisansM.Sc

    A generic face processing framework: technologies, analyses and applications.

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    Jang Kim-fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 108-124).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Introduction about Face Processing Framework --- p.4Chapter 1.2.1 --- Basic architecture --- p.4Chapter 1.2.2 --- Face detection --- p.5Chapter 1.2.3 --- Face tracking --- p.6Chapter 1.2.4 --- Face recognition --- p.6Chapter 1.3 --- The scope and contributions of the thesis --- p.7Chapter 1.4 --- The outline of the thesis --- p.8Chapter 2 --- Facial Feature Representation --- p.10Chapter 2.1 --- Facial feature analysis --- p.10Chapter 2.1.1 --- Pixel information --- p.11Chapter 2.1.2 --- Geometry information --- p.13Chapter 2.2 --- Extracting and coding of facial feature --- p.14Chapter 2.2.1 --- Face recognition --- p.15Chapter 2.2.2 --- Facial expression classification --- p.38Chapter 2.2.3 --- Other related work --- p.44Chapter 2.3 --- Discussion about facial feature --- p.48Chapter 2.3.1 --- Performance evaluation for face recognition --- p.49Chapter 2.3.2 --- Evolution of the face recognition --- p.52Chapter 2.3.3 --- Evaluation of two state-of-the-art face recog- nition methods --- p.53Chapter 2.4 --- Problem for current situation --- p.58Chapter 3 --- Face Detection Algorithms and Committee Ma- chine --- p.61Chapter 3.1 --- Introduction about face detection --- p.62Chapter 3.2 --- Face Detection Committee Machine --- p.64Chapter 3.2.1 --- Review of three approaches for committee machine --- p.65Chapter 3.2.2 --- The approach of FDCM --- p.68Chapter 3.3 --- Evaluation --- p.70Chapter 4 --- Facial Feature Localization --- p.73Chapter 4.1 --- Algorithm for gray-scale image: template match- ing and separability filter --- p.73Chapter 4.1.1 --- Position of face and eye region --- p.74Chapter 4.1.2 --- Position of irises --- p.75Chapter 4.1.3 --- Position of lip --- p.79Chapter 4.2 --- Algorithm for color image: eyemap and separa- bility filter --- p.81Chapter 4.2.1 --- Position of eye candidates --- p.81Chapter 4.2.2 --- Position of mouth candidates --- p.83Chapter 4.2.3 --- Selection of face candidates by cost function --- p.84Chapter 4.3 --- Evaluation --- p.85Chapter 4.3.1 --- Algorithm for gray-scale image --- p.86Chapter 4.3.2 --- Algorithm for color image --- p.88Chapter 5 --- Face Processing System --- p.92Chapter 5.1 --- System architecture and limitations --- p.92Chapter 5.2 --- Pre-processing module --- p.93Chapter 5.2.1 --- Ellipse color model --- p.94Chapter 5.3 --- Face detection module --- p.96Chapter 5.3.1 --- Choosing the classifier --- p.96Chapter 5.3.2 --- Verifying the candidate region --- p.97Chapter 5.4 --- Face tracking module --- p.99Chapter 5.4.1 --- Condensation algorithm --- p.99Chapter 5.4.2 --- Tracking the region using Hue color model --- p.101Chapter 5.5 --- Face recognition module --- p.102Chapter 5.5.1 --- Normalization --- p.102Chapter 5.5.2 --- Recognition --- p.103Chapter 5.6 --- Applications --- p.104Chapter 6 --- Conclusion --- p.106Bibliography --- p.10

    Uncooled infrared imaging face recognition using kernel-based feature vector selection

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    A considerable amount of research has been recently conducted on face recognition tasks, due to increasing demands for security and authentication applications. Recent technological developments in uncooled IR imagery technology have boosted IR face recognition research applications. Our study is part of an on-going research initiated at the Naval Postgraduate School that considers an uncooled low-resolution and low-cost IR camera used for face recognition applications. This work investigates a recent approach which approximates nonlinear kernel-based methods at a significantly reduced computational cost. Our research was applied to an IR database. Results show that this scheme may perform sufficiently close to its â kernelizedâ version considered in a previous study, at a fraction of the computational cost, provided that the associated parameters are well tuned. The thesis considers a relative comparison between the two algorithms, based on identification and verification experiments and considers a statistical test to investigate whether classification performance differences may be considered statistically significant. Results show that, from a cost perspective, a low-resolution uncooled IR camera in conjunction with a low computational-cost classification scheme can be embedded in a robust face recognition system to efficiently address the issue of authentication in security-related tasks.http://archive.org/details/uncooledinfrared109452672Approved for public release; distribution is unlimited

    Uncooled infrared imaging face recognition using kernel-based feature vector selection

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    A considerable amount of research has been recently conducted on face recognition tasks, due to increasing demands for security and authentication applications. Recent technological developments in uncooled IR imagery technology have boosted IR face recognition research applications. Our study is part of an on-going research initiated at the Naval Postgraduate School that considers an uncooled low-resolution and low-cost IR camera used for face recognition applications. This work investigates a recent approach which approximates nonlinear kernel-based methods at a significantly reduced computational cost. Our research was applied to an IR database. Results show that this scheme may perform sufficiently close to its â kernelizedâ version considered in a previous study, at a fraction of the computational cost, provided that the associated parameters are well tuned. The thesis considers a relative comparison between the two algorithms, based on identification and verification experiments and considers a statistical test to investigate whether classification performance differences may be considered statistically significant. Results show that, from a cost perspective, a low-resolution uncooled IR camera in conjunction with a low computational-cost classification scheme can be embedded in a robust face recognition system to efficiently address the issue of authentication in security-related tasks

    HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING

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    Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of ρ\rho and η\eta prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked

    HYPERSPECTRAL IMAGING AND PATTERN RECOGNITION TECHNOLOGIES FOR REAL TIME FRUIT SAFETY AND QUALITY INSPECTION

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    Hyperspectral band selection and band combination has become a powerful tool and have gained enormous interest among researchers. An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. An integrated principal component analysis (PCA) and Fisher linear discriminant (FLD) method has been developed for feature band selection, and other pattern recognition technologies have been applied and compared with the developed method. The results on different types of defects from cucumber and apple samples show that the integrated PCA-FLD method outperforms PCA, FLD and canonical discriminant methods when they are used separately for classification. The integrated method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications. Dimensionality reduction not only serves as the first step of data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirement for online automatic inspection of fruit. In this study, the hyperspectral research shows that the near infrared spectrum at 753nm is best for detecting apple defect. When applied for online apple defect inspection, over 98% of good apple detection rate is achieved. However, commercially available apple sorting and inspection machines cannot effectively solve the stem-calyx problems involved in automatic apple defects detection. In this study, a dual-spectrum NIR/MIR sensing method is applied. This technique can effectively distinguish true defects from stems and calyxes, which leads to a potential solution of the problem. The results of this study will advance the technology in fruit safety and quality inspection and improve the cost-effectiveness of fruit packing processes
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