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- All Subjects: Electrical Engineering
- Creators: Chakrabarti, Chaitali
- Member of: Theses and Dissertations
- Status: Published
Description
Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
ContributorsJiang, Jiewei (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
ContributorsShah, Mohit (Author) / Spanias, Andreas (Thesis advisor) / Chakrabarti, Chaitali (Thesis advisor) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The aging process due to Bias Temperature Instability (both NBTI and PBTI) and Channel Hot Carrier (CHC) is a key limiting factor of circuit lifetime in CMOS design. Threshold voltage shift due to BTI is a strong function of stress voltage and temperature complicating stress and recovery prediction. This poses a unique challenge for long-term aging prediction for wide range of stress patterns. Traditional approaches usually resort to an average stress waveform to simplify the lifetime prediction. They are efficient, but fail to capture circuit operation, especially under dynamic voltage scaling (DVS) or in analog/mixed signal designs where the stress waveform is much more random. This work presents a suite of modelling solutions for BTI that enable aging simulation under all possible stress conditions. Key features of this work are compact models to predict BTI aging based on Reaction-Diffusion theory when the stress voltage is varying. The results to both reaction-diffusion (RD) and trapping-detrapping (TD) mechanisms are presented to cover underlying physics. Silicon validation of these models is performed at 28nm, 45nm and 65nm technology nodes, at both device and circuit levels. Efficient simulation leveraging the BTI models under DVS and random input waveform is applied to both digital and analog representative circuits such as ring oscillators and LNA. Both physical mechanisms are combined into a unified model which improves prediction accuracy at 45nm and 65nm nodes. Critical failure condition is also illustrated based on NBTI and PBTI at 28nm. A comprehensive picture for duty cycle shift is shown. DC stress under clock gating schemes results in monotonic shift in duty cycle which an AC stress causes duty cycle to converge close to 50% value. Proposed work provides a general and comprehensive solution to aging analysis under random stress patterns under BTI.
Channel hot carrier (CHC) is another dominant degradation mechanism which affects analog and mixed signal circuits (AMS) as transistor operates continuously in saturation condition. New model is proposed to account for e-e scattering in advanced technology nodes due to high gate electric field. The model is validated with 28nm and 65nm thick oxide data for different stress voltages. It demonstrates shift in worst case CHC condition to Vgs=Vds from Vgs=0.5Vds. A novel iteration based aging simulation framework for AMS designs is proposed which eliminates limitation for conventional reliability tools. This approach helps us identify a unique positive feedback mechanism termed as Bias Runaway. Bias runaway, is rapid increase of the bias voltage in AMS circuits which occurs when the feedback between the bias current and the effect of channel hot carrier turns into positive. The degradation of CHC is a gradual process but under specific circumstances, the degradation rate can be dramatically accelerated. Such a catastrophic phenomenon is highly sensitive to the initial operation condition, as well as transistor gate length. Based on 65nm silicon data, our work investigates the critical condition that triggers bias runaway, and the impact of gate length tuning. We develop new compact models as well as the simulation methodology for circuit diagnosis, and propose design solutions and the trade-offs to avoid bias runaway, which is vitally important to reliable AMS designs.
Channel hot carrier (CHC) is another dominant degradation mechanism which affects analog and mixed signal circuits (AMS) as transistor operates continuously in saturation condition. New model is proposed to account for e-e scattering in advanced technology nodes due to high gate electric field. The model is validated with 28nm and 65nm thick oxide data for different stress voltages. It demonstrates shift in worst case CHC condition to Vgs=Vds from Vgs=0.5Vds. A novel iteration based aging simulation framework for AMS designs is proposed which eliminates limitation for conventional reliability tools. This approach helps us identify a unique positive feedback mechanism termed as Bias Runaway. Bias runaway, is rapid increase of the bias voltage in AMS circuits which occurs when the feedback between the bias current and the effect of channel hot carrier turns into positive. The degradation of CHC is a gradual process but under specific circumstances, the degradation rate can be dramatically accelerated. Such a catastrophic phenomenon is highly sensitive to the initial operation condition, as well as transistor gate length. Based on 65nm silicon data, our work investigates the critical condition that triggers bias runaway, and the impact of gate length tuning. We develop new compact models as well as the simulation methodology for circuit diagnosis, and propose design solutions and the trade-offs to avoid bias runaway, which is vitally important to reliable AMS designs.
ContributorsSutaria, Ketul (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Chakrabarti, Chaitali (Committee member) / Yu, Shimeng (Committee member) / Arizona State University (Publisher)
Created2014
Description
Three dimensional (3-D) ultrasound is safe, inexpensive, and has been shown to drastically improve system ease-of-use, diagnostic efficiency, and patient throughput. However, its high computational complexity and resulting high power consumption has precluded its use in hand-held applications.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
ContributorsYang, Ming (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Karam, Lina (Committee member) / Frakes, David (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Ozev, Sule (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
Stream processing has emerged as an important model of computation especially in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and are mainly characterized by real-time constraints that demand high throughput and data bandwidth with limited global data reuse. Conventional architectures fail to meet these demands due to their poorly matched execution models and the overheads associated with instruction and data movements.
This work presents StreamWorks, a multi-core embedded architecture for energy-efficient stream computing. The basic processing element in the StreamWorks architecture is the StreamEngine (SE) which is responsible for iteratively executing a stream kernel. SE introduces an instruction locking mechanism that exploits the iterative nature of the kernels and enables fine-grain instruction reuse. Each instruction in a SE is locked to a Reservation Station (RS) and revitalizes itself after execution; thus never retiring from the RS. The entire kernel is hosted in RS Banks (RSBs) close to functional units for energy-efficient instruction delivery. The dataflow semantics of stream kernels are captured by a context-aware dataflow execution mode that efficiently exploits the Instruction Level Parallelism (ILP) and Data-level parallelism (DLP) within stream kernels.
Multiple SEs are grouped together to form a StreamCluster (SC) that communicate via a local interconnect. A novel software FIFO virtualization technique with split-join functionality is proposed for efficient and scalable stream communication across SEs. The proposed communication mechanism exploits the Task-level parallelism (TLP) of the stream application. The performance and scalability of the communication mechanism is evaluated against the existing data movement schemes for scratchpad based multi-core architectures. Further, overlay schemes and architectural support are proposed that allow hosting any number of kernels on the StreamWorks architecture. The proposed oevrlay schemes for code management supports kernel(context) switching for the most common use cases and can be adapted for any multi-core architecture that use software managed local memories.
The performance and energy-efficiency of the StreamWorks architecture is evaluated for stream kernel and application benchmarks by implementing the architecture in 45nm TSMC and comparison with a low power RISC core and a contemporary accelerator.
This work presents StreamWorks, a multi-core embedded architecture for energy-efficient stream computing. The basic processing element in the StreamWorks architecture is the StreamEngine (SE) which is responsible for iteratively executing a stream kernel. SE introduces an instruction locking mechanism that exploits the iterative nature of the kernels and enables fine-grain instruction reuse. Each instruction in a SE is locked to a Reservation Station (RS) and revitalizes itself after execution; thus never retiring from the RS. The entire kernel is hosted in RS Banks (RSBs) close to functional units for energy-efficient instruction delivery. The dataflow semantics of stream kernels are captured by a context-aware dataflow execution mode that efficiently exploits the Instruction Level Parallelism (ILP) and Data-level parallelism (DLP) within stream kernels.
Multiple SEs are grouped together to form a StreamCluster (SC) that communicate via a local interconnect. A novel software FIFO virtualization technique with split-join functionality is proposed for efficient and scalable stream communication across SEs. The proposed communication mechanism exploits the Task-level parallelism (TLP) of the stream application. The performance and scalability of the communication mechanism is evaluated against the existing data movement schemes for scratchpad based multi-core architectures. Further, overlay schemes and architectural support are proposed that allow hosting any number of kernels on the StreamWorks architecture. The proposed oevrlay schemes for code management supports kernel(context) switching for the most common use cases and can be adapted for any multi-core architecture that use software managed local memories.
The performance and energy-efficiency of the StreamWorks architecture is evaluated for stream kernel and application benchmarks by implementing the architecture in 45nm TSMC and comparison with a low power RISC core and a contemporary accelerator.
ContributorsPanda, Amrit (Author) / Chatha, Karam S. (Thesis advisor) / Wu, Carole-Jean (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis report aims at introducing the background of QR decomposition and its application. QR decomposition using Givens rotations is a efficient method to prevent directly matrix inverse in solving least square minimization problem, which is a typical approach for weight calculation in adaptive beamforming. Furthermore, this thesis introduces Givens rotations algorithm and two general VLSI (very large scale integrated circuit) architectures namely triangular systolic array and linear systolic array for numerically QR decomposition. To fulfill the goal, a 4 input channels triangular systolic array with 16 bits fixed-point format and a 5 input channels linear systolic array are implemented on FPGA (Field programmable gate array). The final result shows that the estimated clock frequencies of 65 MHz and 135 MHz on post-place and route static timing report could be achieved using Xilinx Virtex 6 xc6vlx240t chip. Meanwhile, this report proposes a new method to test the dynamic range of QR-D. The dynamic range of the both architectures can be achieved around 110dB.
ContributorsYu, Hanguang (Author) / Bliss, Daniel W (Thesis advisor) / Ying, Lei (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
Driven by stringent power and thermal constraints, heterogeneous multi-core processors, such as the ARM big-LITTLE architecture, are becoming increasingly popular. In this thesis, the use of low-power heterogeneous multi-cores as Microservers using web search as a motivational application is addressed. In particular, I propose a new family of scheduling policies for heterogeneous microservers that assign incoming search queries to available cores so as to optimize for performance metrics such as mean response time and service level agreements, while guaranteeing thermally-safe operation. Thorough experimental evaluations on a big-LITTLE platform demonstrate, on an heterogeneous eight-core Samsung Exynos 5422 MpSoC, with four big and little cores each, that naive performance oriented scheduling policies quickly result in thermal instability, while the proposed policies not only reduce peak temperature but also achieve 4.8x reduction in processing time and 5.6x increase in energy efficiency compared to baseline scheduling policies.
ContributorsJain, Sankalp (Author) / Ogras, Umit Y. (Thesis advisor) / Garg, Siddharth (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2015
Description
Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal
visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.
The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.
In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images
acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new
similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector
exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.
The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.
In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images
acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new
similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector
exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
ContributorsXu, Qian (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this thesis we consider the problem of facial expression recognition (FER) from video sequences. Our method is based on subspace representations and Grassmann manifold based learning. We use Local Binary Pattern (LBP) at the frame level for representing the facial features. Next we develop a model to represent the video sequence in a lower dimensional expression subspace and also as a linear dynamical system using Autoregressive Moving Average (ARMA) model. As these subspaces lie on Grassmann space, we use Grassmann manifold based learning techniques such as kernel Fisher Discriminant Analysis with Grassmann kernels for classification. We consider six expressions namely, Angry (AN), Disgust (Di), Fear (Fe), Happy (Ha), Sadness (Sa) and Surprise (Su) for classification. We perform experiments on extended Cohn-Kanade (CK+) facial expression database to evaluate the expression recognition performance. Our method demonstrates good expression recognition performance outperforming other state of the art FER algorithms. We achieve an average recognition accuracy of 97.41% using a method based on expression subspace, kernel-FDA and Support Vector Machines (SVM) classifier. By using a simpler classifier, 1-Nearest Neighbor (1-NN) along with kernel-FDA, we achieve a recognition accuracy of 97.09%. We find that to process a group of 19 frames in a video sequence, LBP feature extraction requires majority of computation time (97 %) which is about 1.662 seconds on the Intel Core i3, dual core platform. However when only 3 frames (onset, middle and peak) of a video sequence are used, the computational complexity is reduced by about 83.75 % to 260 milliseconds at the expense of drop in the recognition accuracy to 92.88 %.
ContributorsYellamraju, Anirudh (Author) / Chakrabarti, Chaitali (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2014