Browsing by Subject "Face recognition"

Now showing 1 - 20 of 20

Open Access
Big-data streaming applications scheduling based on staged multi-armed bandits
(Institute of Electrical and Electronics Engineers, 2016) Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M.
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.
Open Access
A face tracking algorithm for user interaction in mobile devices
(IEEE, 2009-09) Bülbül, Abdullah; Çipiloğlu, Zeynep; Çapin, Tolga
A new face tracking algorithm, and a human-computer interaction technique based on this algorithm, are proposed for use on mobile devices. The face tracking algorithm considers the limitations of mobile use case - constrained computational resources and varying environmental conditions. The solution is based on color comparisons and works on images gathered from the front camera of a device. The face tracking system generates 2D face position as an output that can be used for controlling different applications. Two of such applications are also presented in this work; the first example uses face position to determine the viewpoint, and the second example enables an intuitive way of browsing large images. © 2009 IEEE.
Open Access
FAME: Face association through model evolution
(IEEE, 2015-06) Gölge, Eren; Duygulu, Pınar
We attack the problem of building classifiers for public faces from web images collected through querying a name. The search results are very noisy even after face detection, with several irrelevant faces corresponding to other people. Moreover, the photographs are taken in the wild with large variety in poses and expressions. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the models associated to a name to evolve. The idea is based on capturing discriminative and representative properties of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with different characteristics. On benchmark datasets, our results are comparable to or better than the state-of-the-art studies for the task of face identification. © 2015 IEEE.
Open Access
Finding faces in news photos using both face and name information
(IEEE, 2006) Özkan, Derya; Duygulu, Pınar
We propose a method to associate names and faces for querying people in large news photo collections. On the assumption that a person's face is likely to appear when his/her name is mentioned in the caption, first all the faces associated with the query name are selected, Among these faces, there could be many faces corresponding to the queried person in different conditions, poses and times, but there could also be other faces corresponding to other people in the caption or some non-face images due to the errors in the face detection method used, However, in most cases, the number of corresponding faces of the queried person will be large, and these faces will be more similar to each other than to others. When the similarities of faces are represented in a graph structure, the set of most similar faces will be the densest component in the graph. In this study, we propose a graph-based method to find the most similar subset among the set of possible faces associated with the query name, where the most similar subset is likely to correspond to the faces of the queried person. © 2006 IEEE.
Open Access
Finding people frequently appearing in news
(Springer, 2006-07) Özkan, Derya; Duygulu, Pınar
We propose a graph based method to improve the performance of person queries in large news video collections. The method benefits from the multi-modal structure of videos and integrates text and face information. Using the idea that a person appears more frequently when his/her name is mentioned, we first use the speech transcript text to limit our search space for a query name. Then, we construct a similarity graph with nodes corresponding to all of the faces in the search space, and the edges corresponding to similarity of the faces. With the assumption that the images of the query name will be more similar to each other than to other images, the problem is then transformed into finding the densest component in the graph corresponding to the images of the query name. The same graph algorithm is applied for detecting and removing the faces of the anchorpeople in an unsupervised way. The experiments are conducted on 229 news videos provided by NIST for TRECVID 2004. The results show that proposed method outperforms the text only based methods and provides cues for recognition of faces on the large scale. © Springer-Verlag Berlin Heidelberg 2006.
Open Access
A graph based approach for finding people in news
(2007) Özkan, Derya
Along with the recent advances in technology, large quantities of multi-modal data has arisen and became prevalent. Hence, effective and efficient retrieval, organization and analysis of such data constitutes a big challenge. Both news photographs on the web and news videos on television form this kind of data by covering rich sources of information. People are mostly the main subject of the news; therefore, queries related to a specific person are often desired. In this study, we propose a graph based method to improve the performance of person queries in large news video and photograph collections. We exploit the multi-modal structure of the data by associating text and face information. On the assumption that a person’s face is likely to appear when his/her name is mentioned in the news, only the faces associated with the query name are selected first to limit the search space for a query name. Then, we construct a similarity graph of the faces in this limited search space, where nodes correspond to the faces and edges correspond to the similarity between the faces. Among these faces, there could be many faces corresponding to the queried person in different conditions, poses and times. There could also be other faces corresponding to other people in the news or some non-face images due to the errors in the face detection method used. However, in most cases, the number of corresponding faces of the queried person will be large, and these faces will be more similar to each other than to others. To this end, the problem is transformed into a graph problem, in which we seek to find the densest component of the graph. This most similar subset (densest component) is likely to correspond to the faces of the query name. Finally, the result of the graph algorithm is used as a model for further recognition when new faces are encountered. In the paper, it has been shown that the graph approach can also be used for detecting the faces of the anchorpersons without any supervision. The experiments are performed on two different data sets: news photographs and news videos. The first set consists of thousands of news photographs from Yahoo! news web site. The second set includes 229 broadcast news videos provided by NIST for TRECVID 2004. Images from the both sets are taken in real life conditions and, therefore, have a large variety of poses, illuminations and expressions. The results show that proposed method outperforms the text only based methods and provides cues for recognition of faces on the large scale.
Open Access
A graph based approach for naming faces in news photos
(I E E E Computer Society, 2006) Ozkan, D.; Duygulu, P.
We propose a method to associate names and faces for querying people in large news photo collections. On the assumption that a person's face is likely to appear when his/her name is mentioned in the caption, first all the faces associated with the query name are selected. Among these faces, there could be many faces corresponding to the queried person in different conditions, poses and times, but there could also be other faces corresponding to other people in the caption or some non-face images due to the errors in the face detection method used. However, in most cases, the number of corresponding faces of the queried person will be large, and these faces will be more similar to each other than to others. In this study, we propose a graph based method to find the most similar subset among the set of possible faces associated with the query name, where the most similar subset is likely to correspond to the faces of the queried person. When the similarity of faces are represented in a graph structure, the set of most similar faces will be the densest component in the graph. We represent the similarity of faces using SIFT descriptors. The matching interest points on two faces are decided after the application of two constraints, namely the geometrical constraint and the unique match constraint. The average distance of the matching points are used to construct the similarity graph. The most similar set of faces is then found based on a greedy densest component algorithm. The experiments are performed on thousands of news photographs taken in real life conditions and, therefore, having a large variety of poses, illuminations and expressions. © 2006 IEEE.
Open Access
Human face detection in video using edge projections
(SPIE, 2006) Türkan, Mehmet; Dülek, Berkan; Onaran, İbrahim; Çetin, A. Enis
In this paper, a human face detection method in images and video is presented. After determining possible face candidate regions using color information, each region is filtered by a high-pass filter of a wavelet transform. In this way, edges of the region are highlighted, and a caricature-like representation of candidate regions is obtained. Horizontal, vertical and filter-like projections of the region are used as feature signals in dynamic programming (DP) and support vector machine (SVM) based classifiers. It turns out that the support vector machine based classifier provides better detection rates compared to dynamic programming in our simulation studies.
Open Access
Image feature extraction using 2D mel-cepstrum
(IEEE, 2010) Çakır, Serdar; Çetin, A. Enis
In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA approach and original image matrices are individually applied to the Common Matrix Approach (CMA) based face recognition system. For each of these feature extraction methods, recognition rates are obtained in the AR face database, ORL database and Yale database. Experimental results indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA approach and raw image matrices. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.
Open Access
Image feature extraction using compressive sensing
(Springer, 2014) Eleyan, A.; Köse, Kıvanç; Çetin, A. Enis
In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.
Open Access
Improvement of face detection algorithms for news videos
(IEEE, 2005) Ikizler, Nazlı; Duygulu, Pınar
People are the most important subjects in news videos and for proper retrieval of person images, face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due to the huge irregularities and high noise level in the data. This study presents a method that combines skin detection and Schneiderman-Kanade face detection, for improving the face detection performance in news videos for a better retrieval. This method has been tested on TRECVID 2003 dataset and the results are very promising. © 2005 IEEE.
Open Access
Labeling of faces in personal photo albums
(IEEE, 2013) Şener, Emre; Yücel, Utku Can; Aksoy, Sercan; Büyükgebiz, ibrahim; Uzun, Burak; Duygulu, Pınar
In this study, we propose a system for organizing personal photo collections. Motivated with the fact that people related queries are the most desired ones, we propose a method for labeling faces in photographs. After representing the detected faces based on the descriptors extracted around facial features, the similarities between all faces in the dataset are found. When user provides labels for a few set of faces, these labels are carried out to other faces using the automatic labeling process. For this pur pose, we proposed a method based on the confidence decisions of three different methods. The user is allowed to provide feedback to increase the performance. Roth search and browsing mechanisms are provided to the user to get the pictures of single or multiple people. © 2013 IEEE.
Open Access
Mel-and Mellin-cepstral feature extraction algorithms for face recognition
(Oxford University Press, 2011-01-17) Cakir, S.; Çetin, A. Enis
In this article, an image feature extraction method based on two-dimensional (2D) Mellin cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum that is widely used in speech recognition is extended to two-dimensions using both the ordinary 2D Fourier transform and the Mellin transform. The resultant feature matrices are applied to two different classifiers such as common matrix approach and support vector machine to test the performance of the mel-cepstrum- and Mellin-cepstrum-based features. The AR face image database, ORL database, Yale database and FRGC database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum-based method are superior to that obtained using 2D principal component analysis, 2D Fourier-Mellin transform and ordinary image matrix-based face recognition in both classifiers. Experimental results indicate that 2D cepstral analysis can also be used in other image feature extraction problems. © The Author 2010. Published by Oxford University Press on behalf of The British Computer Society.
Open Access
Mel-cepstral feature extraction methods for image representation
(S P I E - International Society for Optical Engineering, 2010-15-09) Çakir, S.; Çetin, A. Enis
An image feature extraction method based on the twodimensional (2-D) mel cepstrum is introduced. The concept of onedimensional mel cepstrum, which is widely used in speech recognition, is extended to 2-D in this article. The feature matrix resulting from the 2-D mel-cepstral analysis are applied to the support-vector-machine classifier with multi-class support to test the performance of the mel-cepstrum feature matrix. The AR, ORL, and Yale face databases are used in experimental studies, which indicate that recognition rates obtained by the 2-D mel-cepstrum method are superior to the recognition rates obtained using 2-D principal-component analysis and ordinary image-matrixbased face recognition. Experimental results show that 2-D mel-cepstral analysis can also be used in other image feature extraction problems. .
Open Access
Mel-cepstral methods for image feature extraction
(IEEE, 2010) Çakır, Serdar; Çetin, A. Enis
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum which is widely used in speech recognition is extended to 2D in this article. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA, 2D PCA and original image matrices are converted to feature vectors and individually applied to a Support Vector Machine (SVM) classification engine for comparison. The AR face database, ORL database, Yale database and FRGC version 2 database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA, 2D PCA and ordinary image matrix based face recognition. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE.
Open Access
Person search made easy
(Springer, Berlin, Heidelberg, 2005) İkizler, Nazlı; Duygulu, Pınar
In this study, we present a method to extensively reduce the number of retrieved images and increase the retrieval performance for the person queries on the broadcast news videos. A multi-modal approach which integrates face and text information is proposed. A state-of-the-art face detection algorithm is improved using a skin color based method to eliminate the false alarms. This pruned set is clustered to group the similar faces and representative faces are selected from each cluster to be provided to the user. For six person queries of TRECVID2004, on the average, the retrieval rate is increased from 8% to around 50%, and the number of images that the user has to inspect are reduced from hundreds and thousands to tens. © Springer-Verlag Berlin Heidelberg 2005.
Open Access
Rule based segmentation and subject identification using fiducial features and subspace projection methods
(Academy Publisher, 2007) Ince, E. A.; Ali, S. A.
This paper describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.
Open Access
Systematic evaluation of face detection algorithms on news videos
(IEEE, 2007) Acar, Can; Atlas, Arda; Çevik, Koray; Ölmez İsa; Ünlü, Mustafa; Özkan, Derya; Duygulu, Pınar
People are the most important subjects in news videos and for proper retrieval of people images; face detection is a very crucial step. However, face detection and recognition in news videos is a very challenging task due to the huge irregularities and high noise level in the data. In addition to that, with different face detection algorithms, the number and the type of the faces may differ. In this study, in order to get the best performance from existing methods, systematic evaluation of these methods is performed. In the experiments, news videos from TRECVID 2006 data set are used and for evaluation four different face detection methods are chosen.
Open Access
Two-dimensional Mellin and mel-cepstrum for image feature extraction
(Springer, Dordrecht, 2010) Çakır, Serdar; Çetin, A. Enis
An image feature extraction method based on two-dimensional (2D)Mellin cepstrum is introduced. The concept of one-dimensional (1D) melcepstrum which is widely used in speech recognition is extended to two-dimensions both using the ordinary 2D Fourier Transform and the Mellin transform in this article. The resultant feature matrices are applied to two different classifiers (Common Matrix Approach and Support Vector Machine) to test the performance of the melcepstrum and Mellincepstrum based features. Experimental studies indicate that recognition rates obtained by the 2D melcepstrum based method are superior to the recognition rates obtained using 2D PCA and ordinary image matrix based face recognition in both classifiers. © 2011 Springer Science+Business Media B.V.
Open Access
Video copy detection using multiple visual cues and MPEG-7 descriptors
(Academic Press, 2010) Küçüktunç, O.; Baştan M.; Güdükbay, Uğur; Ulusoy, Özgür
We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency. © 2010 Elsevier Inc. All rights reserved.