Faisal Shafait

University of Western Australia, Perth City, Western Australia, Australia

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Publications (107)25.23 Total impact

  • Andreas Dengel, F. Shafait
    05/2014: pages 177 - 222; , ISBN: 978-0-85729-858-4
  • Andreas Dengel, Sarah Elkasrawi, Faisal Shafait
    DAS 2014, Tours, France; 04/2014
  • Source
    Naveed Akhtar, Faisal Shafait, Ajmal Mian
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    ABSTRACT: Hyperspectral images obtained from remote sensing platforms have limited spatial resolution. Thus, each spectra measured at a pixel is usually a mixture of many pure spectral signatures (endmembers) corresponding to different materials on the ground. Hyperspectral unmixing aims at separating these mixed spectra into its constituent endmembers. We formulate hyperspectral unmixing as a constrained sparse coding (CSC) problem where unmixing is performed with the help of a library of pure spectral signatures under positivity and summation constraints. We propose two different methods that perform CSC repeatedly over the hyperspectral data. However, the first method, Repeated-CSC (RCSC), systematically neglects a few spectral bands of the data each time it performs the sparse coding. Whereas the second method, Repeated Spectral Derivative (RSD), takes the spectral derivative of the data before the sparse coding stage. The spectral derivative is taken such that it is not operated on a few selected bands. Experiments on simulated and real hyperspectral data and comparison with existing state of the art show that the proposed methods achieve significantly higher accuracy. Our results demonstrate the overall robustness of RCSC to noise and better performance of RSD at high signal to noise ratio.
    IEEE Winter Conference on Applications of Computer Vision; 03/2014
  • Source
    Zohaib Khan, Faisal Shafait, Yiqun Hu, Ajmal Mian
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    ABSTRACT: Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.
    02/2014;
  • Matthias Reif, Faisal Shafait
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    ABSTRACT: Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms are generally categorized into two broad categories: algorithms that do not take the following classifier into account (the filter approaches), and algorithms that evaluate the following classifier for each considered feature subset (the wrapper approaches). Filter approaches are typically faster, but wrapper approaches deliver a higher performance. In this paper, we present the algorithm – Predictive Forward Selection – based on the widely used wrapper approach forward selection. Using ideas from meta-learning, the number of required evaluations of the target classifier is reduced by using experience knowledge gained during past feature selection runs on other datasets. We have evaluated our approach on 59 real-world datasets with a focus on SVM as the target classifier. We present comparisons with state-of-the-art wrapper and filter approaches as well as one embedded method for SVM according to accuracy and run-time. The results show that the presented method reaches the accuracy of traditional wrapper approaches requiring significantly less evaluations of the target algorithm. Moreover, our method achieves statistically significant better results than the filter approaches as well as the embedded method.
    Pattern Recognition. 01/2014; 47(4):1664–1673.
  • Source
    Naveed Akhtar, Faisal Shafait, Ajmal Mian
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    ABSTRACT: Spectra measured at a pixel of a remote sensing hyperspectral sensor is usually a mixture of multiple spectra (end-members) of different materials on the ground. Hyperspectral unmixing aims at identifying the endmembers and their propor-tions (fractional abundances) in the mixed pixels. Hyperspectral unmixing has recently been casted into a sparse approximation problem and greedy sparse approximation approaches are consid-ered desirable for solving it. However, the high correlation among the spectra of different materials seriously affects the accuracy of the greedy algorithms. We propose a greedy sparse approximation algorithm, called SUnGP, for unmixing of hyperspectral data. SUnGP shows high robustness against the correlation of the spectra of materials. The algorithm employes a subspace pruning strategy for the identification of the endmembers. Experiments show that the proposed algorithm not only outperforms the state of the art greedy algorithms, its accuracy is comparable to the algorithms based on the convex relaxation of the problem, but with a considerable computational advantage.
    ICPR; 01/2014
  • Source
    Naveed Akhtar, Faisal Shafait, Ajmal Mian
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    ABSTRACT: Spectra measured at a single pixel of a remotely sensed hyperspectral image is usually a mixture of multiple spectral signatures (endmembers) corresponding to different materials on the ground. Sparse unmixing assumes that a mixed pixel is a sparse linear combination of different spectra already available in a spectral library. It uses sparse approximation techniques to solve the hyperspectral unmixing problem. Among these techniques, greedy algorithms suite well to sparse unmixing. However, their accuracy is immensely compromised by the high correlation of the spectra of different materials. This work proposes a novel greedy algorithm, called OMP-Star, that shows robustness against the high correlation of spectral signatures. We preprocess the signals with spectral derivatives before they are used by the algorithm. To approximate the mixed pixel spectra, the algorithm employs a futuristic greedy approach that, if necessary, considers its future iterations before identifying an endmember. We also extend OMP-Star to exploit the non-negativity of spectral mixing. Experiments on simulated and real hyperspectral data show that the proposed algorithms outperform the state of the art greedy algorithms. Moreover, the proposed approach achieves results comparable to convex relaxation based sparse approximation techniques, while maintaining the advantages of greedy approaches.
    IEEE Transactions on Geoscience and Remote Sensing 01/2014; · 3.47 Impact Factor
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    ABSTRACT: Gender score is the cognitive judgement of the degree of masculinity or femininity of a face which is considered to be a continuum. Gender scores have long been used in psychological studies to understand the complex psychosocial relationships between people. Perceptual scores for gender and attractiveness have been employed for quality assessment and planning of cosmetic facial surgery. Various neurological disorders have been linked to the facial structure in general and the facial gender perception in particular. While, subjective gender scoring by human raters has been a tool of choice for psychological studies for many years, the process is both time and resource consuming. In this study, we investigate the geometric features used by the human cognitive system in perceiving the degree of masculinity/femininity of a 3D face. We then propose a mathematical model that can mimic the human gender perception. For our experiments, we obtained 3D face scans of 64 subjects using the 3dMDface scanner. The textureless 3D face scans of the subjects were then observed in different poses and assigned a gender score by 75 raters of a similar background. Our results suggest that the human cognitive system employs a combination of Euclidean and geodesic distances between biologically significant landmarks of the face for gender scoring. We propose a mathematical model that is able to automatically assign an objective gender score to a 3D face with a correlation of up to 0.895 with the human subjective scores.
    PLoS ONE 01/2014; 9(6):e99483. · 3.73 Impact Factor
  • Source
    Naveed Akhtar, Faisal Shafait, Ajmal Mian
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    ABSTRACT: Existing hyperspectral imaging systems produce low spa-tial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to ex-plain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyper-spectral image. Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hy-perspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.
    European Conference on Computer Vision (ECCV); 01/2014
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    ABSTRACT: Recent advances in computer vision on the one hand, and imaging technologies on the other hand, have opened up a number of interesting possibilities for robust 3-D scene labeling. This paper presents contributions in several directions to improve the state-of-the-art in RGB-D scene labeling. First, we present a novel combination of depth and color features to recognize different object categories in isolation. Then, we use a context model that exploits detection results of other objects in the scene to jointly optimize labels of co-occurring objects in the scene. Finally, we investigate the use of social media mining to develop the context model, and provide an investigation of its convergence. We perform thorough experimentation on both the publicly available RGB-D Dataset from the University of Washington as well as on the NYU scene dataset. An analysis of the results shows interesting insights about contextual object category recognition, and its benefits.
    Robotics and Autonomous Systems 12/2013; · 1.16 Impact Factor
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    ABSTRACT: Authentication of documents can be done by detecting the printing device used to generate the print-out. Many manufacturers of color laser printers and copiers designed their devices in a way to integrate a unique tracking pattern in each print-out. This pattern is used to identify the exact device the print-out originates from. In this paper, we present an important extension of our previous work for (a) detecting the class of printer that was used to generate a print-out, namely automatic methods for (b) comparing two base patterns from two different print-outs to verify if two print-outs come from the same printer and for (c) automatic decoding of the base pattern to extract the serial number and, if available, the time and the date the document was printed. Finally, we present (d) the first public dataset on tracking patterns (also called machine identification codes) containing 1,264 images from 132 different printers. Evaluation on this dataset resulted in accuracies of up to 93.0 % for detecting the printer class. Comparison and decoding of the tracking patterns achieved accuracies of 91.3 and 98.3 %, respectively.
    Formal Pattern Analysis & Applications 11/2013; · 0.81 Impact Factor
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    Adnan Ul-Hasan, Faisal Shafait, Thomas M. Breuel
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    ABSTRACT: Long Short-Term Memory (LSTM) networks have yielded excellent results on handwriting recognition. This paper describes an application of bidirectional LSTM networks to the problem of machine-printed Latin and Fraktur recognition. Latin and Fraktur recognition differs significantly from handwriting recognition in both the statistical properties of the data, as well as in the required, much higher levels of accuracy. Applications of LSTM networks to handwriting recognition use two-dimensional recurrent networks, since the exact position and baseline of handwritten characters is variable. In contrast, for printed OCR, we used a one-dimensional recurrent network combined with a novel algorithm for baseline and x-height normalization. A number of databases were used for training and testing, including the UW3 database, artificially generated and degraded Fraktur text and scanned pages from a book digitization project. The LSTM architecture achieved 0:6% character-level test-set error on English text. When the artificially degraded Fraktur data set is divided into training and test sets, the system achieves an error rate of 1:64%. On specific books printed in Fraktur (not part of the training set), the system achieves error rates of 0:15% (Fontane) and 1:47% (Ersch-Gruber). These recognition accuracies were found without using any language modelling or any other post-processing techniques.
    International Conference on Document Analysis and Recognition, Washington D.C., USA; 08/2013
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    ABSTRACT: Traditionally, stamps are considered as a seal of authenticity for documents. For automatic processing and verification, segmentation of stamps from documents is pivotal. Existing methods for stamp extraction mostly employ color and/or shape based techniques, thereby limiting their applicability to only colored and specific shape stamps. In this paper, a novel, generic method based on part-based features is presented for segmentation of stamps from document images. The proposed method can segment black, colored, unseen, arbitrary shaped, textual, as well as graphical stamps. The proposed method is evaluated on a publicly available dataset for stamp detection and verification and achieved recall and precision of 73% and 83% respectively, for black stamps which were not addressed in the past.
    2013 12th International Conference on Document Analysis and Recognition; 08/2013
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    ABSTRACT: Recurrent neural networks (RNN) have been suc-cessfully applied for recognition of cursive handwritten docu-ments, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15% and for the second is 13.6%. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artefacts along with clean images. Comparison with shape-matching based method is also presented.
    International Conference on Document Analysis and Recognition, Washington D.C., USA; 08/2013
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    ABSTRACT: This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on; 01/2013
  • Z. Khan, F. Shafait, A. Mian
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    ABSTRACT: Ink mismatch detection provides important clues to forensic document examiners by identifying whether a particular handwritten note was written with a specific pen, or to show that some part (e.g. signature) of a note is written with a different ink as compared to the rest of the note. In this paper, we show that a hyper spectral image (HSI) of handwritten notes can discriminate between inks that are visually similar in appearance. For this purpose, we develop the first ever hyper spectral image database of handwritten notes in various blue and black inks, comprising a total of 70 hyper spectral images each in 33 bands of the visible spectrum. In an unsupervised clustering scheme, the spectral responses of inks fall into separate clusters to allow segmentation of two different inks in a questioned document. The same method fails to segment inks correctly when applied to RGB scans of these documents, since the inks are very hard to distinguish in the visible spectral range. HSI overcomes the shortcomings of RGB and allows better discrimination between inks. We further evaluate which subset of bands from HSI is most useful for the purpose of ink mismatch detection. We hope that these findings will stimulate the use of HSI in document analysis research, especially for questioned document examination.
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Long Short-Term Memory (LSTM) networks have yielded excellent results on handwriting recognition. This paper describes an application of bidirectional LSTM networks to the problem of machine-printed Latin and Fraktur recognition. Latin and Fraktur recognition differs significantly from handwriting recognition in both the statistical properties of the data, as well as in the required, much higher levels of accuracy. Applications of LSTM networks to handwriting recognition use two-dimensional recurrent networks, since the exact position and baseline of handwritten characters is variable. In contrast, for printed OCR, we used a one-dimensional recurrent network combined with a novel algorithm for baseline and x-height normalization. A number of databases were used for training and testing, including the UW3 database, artificially generated and degraded Fraktur text and scanned pages from a book digitization project. The LSTM architecture achieved 0.6% character-level test-set error on English text. When the artificially degraded Fraktur data set is divided into training and test sets, the system achieves an error rate of 1.64%. On specific books printed in Fraktur (not part of the training set), the system achieves error rates of 0.15% (Fontane) and 1.47% (Ersch-Gruber). These recognition accuracies were found without using any language modelling or any other post-processing techniques.
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on; 01/2013
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Automatically identifying that a certain page in a set of documents is printed with a different printer than the rest of the documents can give an important clue for a possible forgery attempt. Different printers vary in their produced printing quality, which is especially noticeable at the edges of printed characters. In this paper, a system using the difference in edge roughness to distinguish laser printed ages from inkjet printed pages is presented. Several feature extraction methods have been developed and evaluated for that purpose. In contrast to previous work, this system uses unsupervised anomaly detection to detect documents printed by a different printing technique than the majority of the documents among a set. This approach has the advantage that no prior training using genuine documents has to be done. Furthermore, we created a dataset featuring 1200 document images from different domains (invoices, contracts, scientific papers) printed by 7 different inkjet and 13 laser printers. Results show that the presented feature extraction method achieves the best outlier rank score in comparison to state-of-the-art features.
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on; 01/2013
  • S.H. Khan, Z. Khan, F. Shafait
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    ABSTRACT: Handwritten signatures are one of the most socially acceptable and traditionally used person identification and authentication metric. Although a number of authentication systems based on handwritten signatures have been proposed, a little attention is paid towards employing signatures for person identification. In this work, we address both the identification and verification problems related to analysis of dynamic handwritten signatures. In this way, the need to present username before biometric verification can be eliminated in current signature based biometric authentication systems. A compressed sensing approach is used for user identification and to reject a query signature that does not belong to any user in the database. Once a person is identified, an automatic alignment of query signature with the reference template is carried out such that the correlations between two signature instances are maximized. An elastic distance matching algorithm is then run over the presented data which declares the query signature as either genuine or forged based on the dissimilarity with the reference signature. Our results show that dynamic signatures can be accurately used for person identification along with the traditional verification methods.
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on; 01/2013
  • S.Z. Gilani, F. Shafait, A. Mian
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    ABSTRACT: Automatic gender classification has many applications in human computer interaction. However, to determine the gender of an unseen face is challenging because of the diversity and variations in the human face. In this paper, we explore the importance of biologically significant facial landmarks for gender classification and propose a fully automatic gender classification algorithm. We extract 3D Euclidean and Geodesic distances between these landmarks and use feature selection to determine the relative importance of the biological landmarks for classifying gender. Unlike existing techniques, our algorithm is fully automatic since all landmarks are automatically detected. Experiments on one of the largest 3D face databases FRGC v2 show that our algorithm outperforms all existing techniques by a significant margin.
    Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on; 01/2013

Publication Stats

533 Citations
25.23 Total Impact Points

Institutions

  • 2013–2014
    • University of Western Australia
      Perth City, Western Australia, Australia
  • 2006–2013
    • Deutsches Forschungszentrum für Künstliche Intelligenz
      Kaiserlautern, Rheinland-Pfalz, Germany
    • Technische Universität Hamburg-Harburg
      Hamburg, Hamburg, Germany
    • University of Engineering and Technology, Taxila
      • Department of Electrical Engineering
      Islāmābād, Islamabad Capital Territory, Pakistan
  • 2006–2011
    • Technische Universität Kaiserslautern
      • AG Bildverstehen und Mustererkennung
      Kaiserslautern, Rhineland-Palatinate, Germany