The Experts below are selected from a list of 12942 Experts worldwide ranked by ideXlab platform

Marios Savvides - One of the best experts on this subject based on the ideXlab platform.

  • Frequency Domain Face Recognition
    'IntechOpen', 2021
    Co-Authors: Marios Savvides, Ramamurthy Bhagavatula, Ramzi Abiantun
    Abstract:

    We have shown through the course of this chapter that the Fourier or Frequency Domain of facial data contains significantly more useful information when processed than its spatial counterpart. The simple coupling of standard algorithms such as Eigenfaces and Fisherfaces with Frequency Domain Representation of phase and magnitude spectrums, can result in noticeable improvements in performance as we have shown for pose and illumination tolerance. Evolving our intuition about the Frequency Domain leads us to the group of algorithms collectively referred as ACFs. Primarily originating from Frequency Domain interpretations of data, ACFs allow for significant discriminative ability while providing other attractive qualities such as shift invariance, noise tolerance, and graceful degredation. As the presented results indicate, ACFs are capable of performing highly accurate face recognition in varying and challenging circumstances. In particular, the presented work also demonstrates the compatibility of ACFs with other algorithms allowing them to be easily integrated into most face recognition systems. Frequency Domain related algorithms, particularly ACFs, still hold much potential in advancing the area of face recognition and biometrics in general. Our proposed future work spans the broad horizon of face recognition including but not limited to improved general face recognition, large scale applications, improved illumination tolerance, hardware implementations, and privacy issues. The last area mentioned holds great significance in today’s digital world. Although biometrics are gaining popularity as a reliable and secure method of authentication and identification, they are as susceptible to loss as typical ciphers or passwords. Represented as digital data, a biometric template can be stolen and as an almost unique identifier of a person cannot be replaced. To this end, cancellable biometrics are being developed to allow re-usability and re-issuement of biometrics using encryption type methods and performing the recognition in the encrypted Domain. ACFs easily integrate into the scheme of cancellable biometrics (Jain & Uludag, 2003; Savvides et al.

  • human face identification from video based on Frequency Domain asymmetry Representation using hidden markov models
    ACM Multimedia, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    In this paper we introduce a novel human face identification scheme from video data based on a Frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.

  • face identification using novel Frequency Domain Representation of facial asymmetry
    IEEE Transactions on Information Forensics and Security, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the Frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the Frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit Frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented

  • improved human face identification using Frequency Domain Representation of facial asymmetry
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides
    Abstract:

    This paper explores the role of facial asymmetry in identification tasks using a Frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier which establishes the robustness of these measures to intra-personal distortions. We next demonstrate that it is possible to even improve upon these results by simple means. In particular, we use two methods, namely, feature set combination and statistical resampling methods like bagging, which attains perfect classification results (0% error rate) in some cases. Both these methods require very few additional resources in terms of computing power, hence they are useful for practical applications as well.

  • using feature combination and statistical resampling for accurate face recognition based on Frequency Domain Representation of facial asymmetry
    International Conference on Automatic Face and Gesture Recognition, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides
    Abstract:

    This paper explores the efficiency of facial asymmetry in face identification tasks using a Frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and random subspace method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of Frequency Domain Representation of facial asymmetry in automatic identification tasks

Sinjini Mitra - One of the best experts on this subject based on the ideXlab platform.

  • human face identification from video based on Frequency Domain asymmetry Representation using hidden markov models
    ACM Multimedia, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    In this paper we introduce a novel human face identification scheme from video data based on a Frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.

  • face identification using novel Frequency Domain Representation of facial asymmetry
    IEEE Transactions on Information Forensics and Security, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the Frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the Frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit Frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented

  • improved human face identification using Frequency Domain Representation of facial asymmetry
    International Conference on Acoustics Speech and Signal Processing, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides
    Abstract:

    This paper explores the role of facial asymmetry in identification tasks using a Frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier which establishes the robustness of these measures to intra-personal distortions. We next demonstrate that it is possible to even improve upon these results by simple means. In particular, we use two methods, namely, feature set combination and statistical resampling methods like bagging, which attains perfect classification results (0% error rate) in some cases. Both these methods require very few additional resources in terms of computing power, hence they are useful for practical applications as well.

  • using feature combination and statistical resampling for accurate face recognition based on Frequency Domain Representation of facial asymmetry
    International Conference on Automatic Face and Gesture Recognition, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides
    Abstract:

    This paper explores the efficiency of facial asymmetry in face identification tasks using a Frequency Domain Representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and random subspace method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of Frequency Domain Representation of facial asymmetry in automatic identification tasks

  • analyzing asymmetry biometric in the Frequency Domain for face recognition
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: Sinjini Mitra, Marios Savvides
    Abstract:

    The paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the Frequency Domain. In particular, we show that these biometrics work well for images showing expression variations. A comparison of the recognition rates with those obtained from spatial Domain asymmetry measures based on raw intensity values suggests that the Frequency Domain Representation is more robust to intra-personal distortions and, indeed, provides an efficient approach for performing classification or recognition. The role of asymmetry of the different regions (e.g., eyes, mouth, nose) of the face is investigated to determine which regions provide the maximum discrimination among individuals in the presence of different expressions for better classification results in such a scenario.

B Vijaya V K Kumar - One of the best experts on this subject based on the ideXlab platform.

  • human face identification from video based on Frequency Domain asymmetry Representation using hidden markov models
    ACM Multimedia, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    In this paper we introduce a novel human face identification scheme from video data based on a Frequency Domain Representation of facial asymmetry. A Hidden Markov Model (HMM) is used to learn the temporal dynamics of the training video sequences of each subject and classification of the test video sequences is performed using the likelihood scores obtained from the HMMs. We apply this method to a video database containing 55 subjects showing extreme expression variations and demonstrate that the HMM-based method performs much better than identification based on the still images using an Individual PCA (IPCA) classifier, achieving more than 30% improvement.

  • face identification using novel Frequency Domain Representation of facial asymmetry
    IEEE Transactions on Information Forensics and Security, 2006
    Co-Authors: Sinjini Mitra, Marios Savvides, B Vijaya V K Kumar
    Abstract:

    Face recognition is a challenging task. This paper introduces a novel set of biometrics, defined in the Frequency Domain and representing a form of "facial asymmetry." A comparison with existing spatial asymmetry measures suggests that the Frequency-Domain Representation provides an efficient approach for performing human identification in the presence of severe expressions and for expression classification. Error rates of less than 5% are observed for human identification and around 25% for expression classification on a database of 55 individuals. Feature analysis indicates that asymmetry of the different face parts helps in these two apparently conflicting classification problems. An interesting connection between asymmetry and the Fourier Domain phase spectra is then established. Finally, a compact one-bit Frequency-Domain Representation of asymmetry is introduced, and a simplistic Hamming distance classifier is shown to be more efficient than traditional classifiers from storage and the computation point of view, while producing equivalent human identification results. In addition, the application of these compact measures to verification and a statistical analysis are presented

Marc Levoy - One of the best experts on this subject based on the ideXlab platform.

  • Frequency Domain volume rendering
    International Conference on Computer Graphics and Interactive Techniques, 1993
    Co-Authors: Takashi Totsuka, Marc Levoy
    Abstract:

    The Fourier projection-slice theorem allos projections of volume data to be generated in O(nsquare log n) time for a volumbe of size ncube. The method operates by extracting and inverse Fourier transforming 2D slices from a 3D Frequency Domain Representation of the volume. Unfortunately, these projections do not exhibit the occlusion that is characteristic of conventional volume renderings. We present a new Frequency Domain volume rendering algorithm that replaces much of the missing depth and shape cues by performing shading calculations in the Frequency Domain during slice extraction. In particular, we demonstrate Frequency Domain methods for computing linear or nonlinear depth cueing and directional diffuse reflection. The resulting images can be generated an order of magnitude faster than volume renderings and may be more useful for many applications.

  • volume rendering using the fourier projection slice theorem
    Graphics Interface, 1992
    Co-Authors: Marc Levoy
    Abstract:

    The Fourier projection-slice theorem states that the inverse transform of a slice extracted from the Frequency Domain Representation of a volume yields a projection of the volume in a direction perpendicular to the slice. This theorem allows the generation of attenuation-only renderings of volume data in O (N 2 log N) time for a volume of size N . In this paper, we show how more realistic renderings can be generated using a class of shading models whose terms are Fourier projections. Models are derived for rendering depth cueing by linear attenuation of variable energy emitters and for rendering directional shading by Lambertian reflection with hemispherical illumination. While the resulting images do not exhibit the occlusion that is characteristic of conventional volume rendering, they provide sufficient depth and shape cues to give a strong illusion that occlusion exists.

Yuichi Tanaka - One of the best experts on this subject based on the ideXlab platform.

  • graph signal denoising via trilateral filter on graph spectral Domain
    IEEE Transactions on Signal and Information Processing over Networks, 2016
    Co-Authors: Masaki Onuki, Masao Yamagishi, Yuichi Tanaka
    Abstract:

    This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral Domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its Frequency Domain Representation. To overcome this problem, we establish the graph spectral Domain Representation of the data-dependent filter, i.e., a spectral graph TF (SGTF). This Representation enables us to design an effective graph signal denoising filter with a Tikhonov regularization. Moreover, for the proposed graph denoising filter, we provide a parameter optimization technique to search for a regularization parameter that approximately minimizes the mean squared error w.r.t. the unknown graph signal of interest. Comprehensive experimental results validate our graph signal processing-based approach for images and graph signals.