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

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

Biohashing with local Zernike moments for face verification (Yüz doğrulama için yerel Zernike momentleri ile biyometrik kıyım)

Local Zernike moments (LZM) is a recently proposed image representation scheme that is shown to be successful for face representation. In this study, a face verification system which depends on biometric hashing scheme, that uses LZM features extracted from face images is proposed. With the proposed system, security and user privacy is ensured. Verification performance of the system, in which user specific secret keys are used for biometric hashing, is evaluated in two different scenarios, on the BioSecure face database. In the first scenario, biometric hashing is realized using each user's secret key and %0 equal error is obtained. In the second scenario, in which the secret key of a user is stolen by an adversary, biometric hashes are created using the stolen key and any biometric sample. In this case, the equal error rate increases to %8, 26, which is comparable to equal error rate of %6, 81, where only LZM feature vectors are used for verification

Biohashing with fingerprint spectral minutiae

In recent years, the interest in human authentication has been increasing. Biometrics are one of the easy authentication schemes, however, security and privacy problems limit their widespread usage. Following the interest in privacy protecting biometric authentication, template protection schemes for biometric modalities has increased significantly in order to cope with security and privacy issues. BioHashing, which is based on transforming the biometric template using pseudo-random projections that are generated using a user-specified key or token, has received much attention as it improves verification accuracies over using only the biometric data, allows template revocation and preserves privacy. In our work, we develop a new BioHashing scheme for fingerprints. A fixed-length feature vector is required in order to design a BioHashing scheme. In the literature, most of the studies on fingerprint BioHashing uses features extracted from fingerprint texture. On the other hand, our new BioHashing scheme is based on minutia based feature vectors. We use the spectral minutiae representation for obtaining a fixed-length feature vector for a fingerprint sample. Then, we use a random projection matrix, which is generated from user's key/token, in order to generate a BioHash vector. We propose to randomly project each column of the spectral minutiae feature matrix via a single matrix which allows fast bit string extraction and adaptive quantization. Experiments on FVC2002 databases show the promise of the proposed system for fast and secure verification