Using local temporal features of bounding boxes for walking/running classification

For intelligent surveillance, one of the major tasks to achieve is to recognize activities present in the scene of interest. Human subjects are the most important elements in a surveillance system and it is crucial to classify human actions. In this paper, we tackle the problem of classifying human actions as running or walking in videos. We propose using local temporal features extracted from rectangular boxes that surround the subject of interest in each frame. We test the system using a database of hand-labeled walking and running videos. Our experiments yield a low 2.5% classification error rate using period-based features and the local speed computed using a range of frames around the current frame. Shorter range time-derivative features are not very useful since they are highly variable. Our results show that the system is able to correctly recognize running or walking activities despite differences in appearance and clothing of subjects

Yerel görünüm tabanlı yüz tanıma için değişik boyut indirme ve normalizasyon yöntemlerinin incelenmesi (Investigation of different dimension reduction and normalization methods for local appearance-based face recognition)

Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these local features, several normalization methods are applied in an attempt to eliminate the changes in lighting conditions and contrast differences among blocks of different face images. The experimental results show the improvements in recognition rates due to the effects of dimension reduction and normalization for three different classifiers. Usage of trainable dimension reduction methods instead of DCT and a new normalization method in our work (within-block normalization as referred in this paper) are two factors that makes difference from previous works in literature. The best performance is achieved using a block size of 16times16, performing dimension reduction using approximate pairwise accuracy criterion (aPAC) and applying within-block mean and variance normalization

Feature extraction and fusion techniques for patch-based face recognition

Face recognition is one of the most addressed pattern recognition problems in recent studies due to its importance in security applications and human computer interfaces. After decades of research in the face recognition problem, feasible technologies are becoming available. However, there is still room for improvement for challenging cases. As such, face recognition problem still attracts researchers from image processing, pattern recognition and computer vision disciplines. Although there exists other types of personal identification such as fingerprint recognition and retinal/iris scans, all these methods require the collaboration of the subject. However, face recognition differs from these systems as facial information can be acquired without collaboration or knowledge of the subject of interest. Feature extraction is a crucial issue in face recognition problem and the performance of the face recognition systems depend on the reliability of the features extracted. Previously, several dimensionality reduction methods were proposed for feature extraction in the face recognition problem. In this thesis, in addition to dimensionality reduction methods used previously for face recognition problem, we have implemented recently proposed dimensionality reduction methods on a patch-based face recognition system. Patch-based face recognition is a recent method which uses the idea of analyzing face images locally instead of using global representation, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating weights for the weighted sum rule is proposed in this thesis. On two separate databases, we have conducted both feature fusion and decision fusion experiments and presented recognition accuracies for different dimensionality reduction and normalization methods. Improvements in recognition accuracies are shown and superiority of decision fusion over feature fusion is advocated. Especially in the more challenging AR database, we obtain significantly better results using decision fusion as compared to conventional methods and feature fusion methods

Security/privacy analysis of biometric hashing and template protection for fingerprint minutiae

This thesis has two main parts. The first part deals with security and privacy analysis of biometric hashing. The second part introduces a method for fixed-length feature vector extraction and hash generation from fingerprint minutiae. The upsurge of interest in biometric systems has led to development of biometric template protection methods in order to overcome security and privacy problems. Biometric hashing produces a secure binary template by combining a personal secret key and the biometric of a person, which leads to a two factor authentication method. This dissertation analyzes biometric hashing both from a theoretical point of view and in regards to its practical application. For theoretical evaluation of biohashes, a systematic approach which uses estimated entropy based on degree of freedom of a binomial distribution is outlined. In addition, novel practical security and privacy attacks against face image hashing are presented to quantify additional protection provided by biometrics in cases where the secret key is compromised (i.e., the attacker is assumed to know the user's secret key). Two of these attacks are based on sparse signal recovery techniques using one-bit compressed sensing in addition to two other minimum-norm solution based attacks. A rainbow attack based on a large database of faces is also introduced. The results show that biometric templates would be in serious danger of being exposed when the secret key is known by an attacker, and the system would be under a serious threat as well. Due to its distinctiveness and performance, fingerprint is preferred among various biometric modalities in many settings. Most fingerprint recognition systems use minutiae information, which is an unordered collection of minutiae locations and orientations Some advanced template protection algorithms (such as fuzzy commitment and other modern cryptographic alternatives) require a fixed-length binary template. However, such a template protection method is not directly applicable to fingerprint minutiae representation which by its nature is of variable size. This dissertation introduces a novel and empirically validated framework that represents a minutiae set with a rotation invariant fixed-length vector and hence enables using biometric template protection methods for fingerprint recognition without signi cant loss in verification performance. The introduced framework is based on using local representations around each minutia as observations modeled by a Gaussian mixture model called a universal background model (UBM). For each fingerprint, we extract a fixed length super-vector of rst order statistics through alignment with the UBM. These super-vectors are then used for learning linear support vector machine (SVM) models per person for verifiation. In addition, the xed-length vector and the linear SVM model are both converted into binary hashes and the matching process is reduced to calculating the Hamming distance between them so that modern cryptographic alternatives based on homomorphic encryption can be applied for minutiae template protection

Graph-based Turkish text normalization and its impact on noisy text processing

User generated texts on the web are freely-available and lucrative sources of data for language technology researchers. Unfortunately, these texts are often dominated by informal writing styles and the language used in user generated content poses processing difficulties for natural language tools. Experienced performance drops and processing issues can be addressed either by adapting language tools to user generated content or by normalizing noisy texts before being processed. In this article, we propose a Turkish text normalizer that maps non-standard words to their appropriate standard forms using a graph-based methodology and a context-tailoring approach. Our normalizer benefits from both contextual and lexical similarities between normalization pairs as identified by a graph-based subnormalizer and a transformation-based subnormalizer. The performance of our normalizer is demonstrated on a tweet dataset in the most comprehensive intrinsic and extrinsic evaluations reported so far for Turkish. In this article, we present the first graph-based solution to Turkish text normalization with a novel context-tailoring approach, which advances the state-of-the-art results by outperforming other publicly available normalizers. For the first time in the literature, we measure the extent to which the accuracy of a Turkish language processing tool is affected by normalizing noisy texts before being processed. An analysis of these extrinsic evaluations that focus on more than one Turkish NLP task (i.e., part-of-speech tagger and dependency parser) reveals that Turkish language tools are not robust to noisy texts and a normalizer leads to remarkable performance improvements once used as a preprocessing tool in this morphologically-rich language.Hazira

Decision Fusion for Patch-Based Face Recognition

Abstract—Patch-based face recognition is a recent method which uses the idea of analyzing face images locally, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating weights for the weighted sum rule is proposed in this paper. Improvements in recognition accuracies are shown and superiority of decision fusion over feature fusion is advocated. In the challenging AR database, we obtain significantly better results using decision fusion as compared to conventional methods and feature fusion methods by using validation accuracy weighting scheme and nearest-neighbor discriminant analysis dimension reduction method. Keywords-face recognition, patch-based face recognition, decision fusion, linear combiner training. I

Fixed-length asymmetric binary hashing for fingerprint verification through GMM-SVM based representations

Fingerprint minutiae information is an unordered and variable-sized collection of minutiae locations and orientations. Advanced template protection algorithms which require a fixed-length binary template cannot operate on minutiae points. In this paper, we propose a novel framework that provides practical solutions that can be used in developing secure fingerprint verification systems. The framework, by using a GMM-SVM fingerprint representation scheme, first generates fixed-length feature vectors from minutiae point sets. The fixed-length representation enables the application of modern cryptographic alternatives based on homomorphic encryption to minutiae template protection. Our framework then utilizes an asymmetric locality sensitive hashing (ALSH) in order to convert the generated fixed-length but real valued GMM-SVM feature vector to a binary bit string. This binarization step transforms the matching process to calculating Hamming distance between binary vectors and expedites fingerprint matching. The verification performance of the framework is evaluated on FVC2002DB1A and DB2A databases

Gözetim videolarında dinamik niteliklere dayalı yürüme/koşma sınıflandırma = Walking/running classification in surveillance videos using dynamic features

Halka açık ortamlardaki gözetim kameraları sayısındaki artış ile birlikte, otomatik nesne ve olay tanıyan sistemlere ihtiyaç da her geçen gün artmaktadır. İnsan aktivitelerinin tanınması ve sınıflandırılması akıllı gözetim sistemlerinin önemli bir parçasıdır. Bu makalede, önemli insan aktiviteleri olan koşma ve yürümenin tanınması problemini ele alıyoruz. Bu problemin çözümü için nesneleri çerçeveleyen kutuların zamana göre değişim bilgisini kullandık. Bu sistemin verimliliğini göstermek amacıyla, farklı insanların aynı aktiviteleri gerçekleştirdiği bir veri kümesi kullanıldı. Deneylerimizde oldukça düşük sınıflandırma hata oranları elde edilmiştir. Bu sonuçlar da gösteriyor ki bu sistem kişilerin fiziksel özelliklerindeki, giyim renklerindeki ve hareket tarzlarındaki değişimlerin de üstesinden gelebilmektedir

Yüz tanıma için ilinti tabanlı yama yerelleştirme (Correlation-based patch localization for face recognition)

With patch-based approaches, it is aimed to tackle the factors such as illumination, pose changes and partial occlusions that are faced in real world applications and complicates the face recognition problem. For patch-based face recognition systems to work robustly, patch locations should correspond to similar image content. In this paper, we propose two patch localization schemes for patch-based face recognition in order to make patch locations to correspond to same area in all of the face images and the image contents of the patches as close as possible. Our experimental results show that with either of the localization schemes, higher recognition results are obtained especially on the partially occluded face images with scarves or sunglasses