579 research outputs found
ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals
The problem of Airwriting Recognition is focused on identifying letters
written by movement of finger in free space. It is a type of gesture
recognition where the dictionary corresponds to letters in a specific language.
In particular, airwriting recognition using sensor data from wrist-worn devices
can be used as a medium of user input for applications in Human-Computer
Interaction (HCI). Recognition of in-air trajectories using such wrist-worn
devices is limited in literature and forms the basis of the current work. In
this paper, we propose an airwriting recognition framework by first encoding
the time-series data obtained from a wearable Inertial Measurement Unit (IMU)
on the wrist as images and then utilizing deep learning-based models for
identifying the written alphabets. The signals recorded from 3-axis
accelerometer and gyroscope in IMU are encoded as images using different
techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF)
and Markov Transition Field (MTF) to form two sets of 3-channel images. These
are then fed to two separate classification models and letter prediction is
made based on an average of the class conditional probabilities obtained from
the two models. Several standard model architectures for image classification
such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been
utilized. Experiments performed on two publicly available datasets demonstrate
the efficacy of the proposed strategy. The code for our implementation will be
made available at https://github.com/ayushayt/ImAiR
Robust Image Recognition Based on a New Supervised Kernel Subspace Learning Method
Fecha de lectura de Tesis Doctoral: 13 de septiembre 2019Image recognition is a term for computer technologies that can recognize certain people, objects or other targeted subjects through the use of algorithms and machine learning concepts. Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only is a nonlinear and complex variation of face images effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. In this research, we particularly focus on face recognition however, two other types of databases rather than face databases are also applied to well investigate the implementation of our algorithm. Experimental results reveal that our method consistently outperforms its competitors across a wide range of dimensionality on all the datasets. SKLDNE method has reached 100 percent of recognition rate for Tn=17 on the Sheffield, 9 on the Yale, 8 on the ORL, 7 on the Finger vein and 11on the Finger Knuckle respectively, while the results are much lower for other methods. This demonstrates the robustness and effectiveness of the proposed method
Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review
Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
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